01 - Introduction.ipynb

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    "# Global imports and settings\n",
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   "source": [
    "# Introduction\n",
    "In this notebook, we will:\n",
    "    \n",
    "- Explain the main machine learning concepts\n",
    "- Used scikit-learn to build a first model\n",
    "- Learn our first algorithm (kNN)"
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    "# Types of machine learning\n",
    "We often distinguish 3 `types` of machine learning:\n",
    "\n",
    "- __Supervised Learning__: learn a model from labeled _training data_, then make predictions\n",
    "- __Unsupervised Learning__: explore the structure of the data to extract meaningful information\n",
    "- __Reinforcement Learning__: develop an agent that improves its performance based on interactions with the environment \n",
    "\n",
    "Note:\n",
    "\n",
    "- Semi-supervised methods combine the first two.\n",
    "- ML systems can combine many types in one system."
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    "## Supervised Machine Learning\n",
    "\n",
    "- Learn a model from labeled training data, then make predictions\n",
    "- Supervised: we know the correct/desired outcome (label)\n",
    "\n",
    "2 subtypes:\n",
    "- Classification: predict a _class label_ (category), e.g. spam/not spam\n",
    "    - Many classifiers can also return a _confidence_ per class\n",
    "- Regression: predict a continuous value, e.g. temperature\n",
    "    - Some algorithms can return a _confidence interval_\n",
    "\n",
    "Most supervised algorithms that we will see can do both."
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   "source": [
    "![types](images/01_supervised.png)"
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    "### Classification\n",
    "\n",
    "- Class labels are discrete, unordered\n",
    "- Can be _binary_ (2 classes) or _multi-class_ (e.g. letter recognition)\n",
    "- Dataset can have any number of predictive variables (predictors)\n",
    "    - Also known as the dimensionality of the dataset\n",
    "- The predictions of the model yield a _decision boundary_ separating the classes"
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   "source": [
    "![classification](images/01_classification.png)"
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   "source": [
    "### Regression\n",
    "\n",
    "- Target variable is numeric\n",
    "- Find the relationship between predictors and the target.\n",
    "    - E.g. relationship between hours studied and final grade\n",
    "- Example: Linear regression (fits a straight line)"
   ]
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   "source": [
    "![regression](images/01_regression2.png)"
   ]
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    "### Reinforcement learning\n",
    "\n",
    "- Develop an agent that improves its performance based on interactions with the environment\n",
    "    - Example: games like Chess, Go,...\n",
    "- _Reward function_ defines how well a (series of) actions works\n",
    "- Learn a series of actions that maximizes reward through exploration"
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   "source": [
    "![reinforcement learning](images/01_rl2.png)"
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   "source": [
    "## Unsupervised Machine Learning\n",
    "\n",
    "- Unlabeled data, or data with unknown structure\n",
    "- Explore the structure of the data to extract information\n",
    "- Many types, we'll just discuss two."
   ]
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    "### Clustering\n",
    "\n",
    "- Organize information into meaningful subgroups (clusters)\n",
    "- Objects in cluster share certain degree of similarity (and dissimilarity to other clusters)\n",
    "- Example: distinguish different types of customers"
   ]
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   "source": [
    "![clustering](images/01_cluster2.png)"
   ]
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   "source": [
    "### Dimensionality reduction\n",
    "\n",
    "- Data can be very high-dimensional and difficult to understand, learn from, store,...\n",
    "- Dimensionality reduction can compress the data into fewer dimensions, while retaining most of the information\n",
    "- Contrary to feature selection, the new features lose their (original) meaning\n",
    "- Is often useful for visualization (e.g. compress to 2D)"
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   "source": [
    "![dimred](images/01_dimred.png)"
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   "source": [
    "## Basic Terminology (on Iris dataset)\n",
    "![terminology](images/01_terminology.png)"
   ]
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   "source": [
    "## Building machine learning systems\n",
    "A typical machine learning system has multiple components:\n",
    "    \n",
    "- Preprocessing: Raw data is rarely ideal for learning\n",
    "    - Feature scaling: bring values in same range\n",
    "    - Encoding: make categorical features numeric\n",
    "    - Discretization: make numeric features categorical\n",
    "    - Feature selection: remove uninteresting/correlated features\n",
    "    - Dimensionality reduction can also make data easier to learn\n",
    "    "
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    "- Learning and model selection\n",
    "    - Every algorithm has its own biases\n",
    "    - No single algorithm is always best (No Free Lunch)\n",
    "    - _Model selection_ compares and selects the best models\n",
    "        - Different algorithms\n",
    "        - Every algorithm has different options (hyperparameters)\n",
    "    - Split data in training and test sets"
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    "- Together they form a _workflow_ of _pipeline_\n",
    "\n",
    "![pipelines](images/01_ml_systems.png)"
   ]
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   "source": [
    "# scikit-learn\n",
    "One of the most prominent Python libraries for machine learning:\n",
    "\n",
    "* Contains many state-of-the-art machine learning algorithms\n",
    "* Offers [comprehensive documentation](http://scikit-learn.org/stable/documentation) about each algorithm\n",
    "* Widely used, and a wealth of [tutorials](http://scikit-learn.org/stable/user_guide.html) and code snippets are available \n",
    "* scikit-learn works well with numpy, scipy, pandas, matplotlib,..."
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   "source": [
    "## Algorithms\n",
    "See the [Reference](http://scikit-learn.org/dev/modules/classes.html)"
   ]
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   "source": [
    "__Supervised learning:__\n",
    "\n",
    "* Linear models (Ridge, Lasso, Elastic Net, ...)\n",
    "* Support Vector Machines\n",
    "* Tree-based methods (Classification/Regression Trees, Random Forests,...)\n",
    "* Nearest neighbors\n",
    "* Neural networks \n",
    "* Gaussian Processes\n",
    "* Feature selection"
   ]
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    "__Unsupervised learning:__\n",
    "    \n",
    "* Clustering (KMeans, ...)\n",
    "* Matrix Decomposition (PCA, ...)\n",
    "* Manifold Learning (Embeddings)\n",
    "* Density estimation\n",
    "* Outlier detection"
   ]
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    "__Model selection and evaluation:__\n",
    "\n",
    "* Cross-validation\n",
    "* Grid-search\n",
    "* Lots of metrics"
   ]
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   "source": [
    "### Data import\n",
    "Multiple options:\n",
    "\n",
    "* A few toy datasets are included in `sklearn.datasets`\n",
    "* You can import data files (CSV) with `pandas` or `numpy`\n",
    "* You can import 1000s of machine learning datasets from OpenML"
   ]
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   "source": [
    "## Example: classification\n",
    "Classify types of Iris flowers (setosa, versicolor, or virginica) based on the flower sepals and petal leave sizes.\n",
    "![Iris image](images/01_iris_with_labels.jpg)"
   ]
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    "Iris is included in scikitlearn, we can just load it.  \n",
    "This will return a `Bunch` object (similar to a `dict`)"
   ]
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     "text": [
      "Keys of iris_dataset: dict_keys(['target_names', 'target', 'data', 'feature_names', 'DESCR'])\n",
      "Iris Plants Database\n",
      "====================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive att\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()\n",
    "\n",
    "print(\"Keys of iris_dataset: {}\".format(iris_dataset.keys()))\n",
    "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
   ]
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   "source": [
    "The targets (classes) and features are stored as `list`s, the data as an `ndarray`"
   ]
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   "cell_type": "code",
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     "text": [
      "Targets: ['setosa' 'versicolor' 'virginica']\n",
      "Features: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "Shape of data: (150, 4)\n",
      "First 5 rows:\n",
      "[[ 5.1  3.5  1.4  0.2]\n",
      " [ 4.9  3.   1.4  0.2]\n",
      " [ 4.7  3.2  1.3  0.2]\n",
      " [ 4.6  3.1  1.5  0.2]\n",
      " [ 5.   3.6  1.4  0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Targets: {}\".format(iris_dataset['target_names']))\n",
    "print(\"Features: {}\".format(iris_dataset['feature_names']))\n",
    "print(\"Shape of data: {}\".format(iris_dataset['data'].shape))\n",
    "print(\"First 5 rows:\\n{}\".format(iris_dataset['data'][:5]))"
   ]
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   "source": [
    "The targets are stored separately as an `ndarray`, with indices pointing to the features"
   ]
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   "execution_count": 33,
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     "text": [
      "Target names: ['setosa' 'versicolor' 'virginica']\n",
      "Targets:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Target names: {}\".format(iris_dataset['target_names']))\n",
    "print(\"Targets:\\n{}\".format(iris_dataset['target']))"
   ]
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   "source": [
    "### Building your first model\n",
    "All scikitlearn classifiers follow the same interface"
   ]
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   "source": [
    "```python\n",
    "class SupervisedEstimator(...):\n",
    "    def __init__(self, hyperparam, ...):\n",
    "\n",
    "    def fit(self, X, y):   # Fit/model the training data\n",
    "        ...                # given data X and targets y\n",
    "        return self\n",
    "     \n",
    "    def predict(self, X):  # Make predictions\n",
    "        ...                # on unseen data X  \n",
    "        return y_pred\n",
    "    \n",
    "    def score(self, X, y): # Predict and compare to true\n",
    "        ...                # labels y                \n",
    "        return score\n",
    "```"
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   "source": [
    "### Training and testing data\n",
    "To evaluate our classifier, we need to test it on unseen data.  \n",
    "`train_test_split`: splits data randomly in 75% training and 25% test data."
   ]
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     "text": [
      "X_train shape: (112, 4)\n",
      "y_train shape: (112,)\n",
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], \n",
    "    random_state=0)\n",
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"y_train shape: {}\".format(y_train.shape))\n",
    "print(\"X_test shape: {}\".format(X_test.shape))\n",
    "print(\"y_test shape: {}\".format(y_test.shape))"
   ]
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   "source": [
    "Note: there are several problems with this approach that we will discuss later:\n",
    "\n",
    "* Why 75%? Are there better ways to split?\n",
    "* What if one random split yields  different models than another?\n",
    "* What if all examples of one class all end up in the training/test set?"
   ]
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   "source": [
    "### Looking at your data\n",
    "We can use a library called `pandas` to easily visualize our data. Note how several features allow to cleanly split the classes."
   ]
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YUbWEys3wA78HPc3d1WlElkG24l1R3vhBzFpQWopb9PHN5hKF229zzsZcDaYx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\nNTUgMCBSIC9Sb290IDEgMCBSID4+CnN0YXJ0eHJlZgoyNjc3NAolJUVPRgo=\n",
      "image/png": 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95sNHJZVsYxuHOERl/ToCCRqAetzK7H2tFN0ZNnqc0JkFXZlmKxO5uVBWRou9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\nZk5+cKIZ9e//CNURXQ4KC2H1atqvvZx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g27fDU0/Bt98G9xFCiQn5fKRkFEBGAS6HHZ/H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\nc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79dQVWVm7Q0B/Pm5TByZP8VhO0tu3Y1RSy22M7tNrHbFUoZYeuS2tOHjx2bREODj6oq\nN/n5iYHtpaXNtLbGd4do27ZG5s/PPeR/h+hwxhm5ZGY6uOCCfweCo46Md+HZ7LS2gqT2wppTp6bx\ny19Op6hI1oqJwWvLloa2u0TWAE6kQR6lrPd+S4uPlpb2ItuK1FRHYH2fJC8RQ5EESUKIbjNNzf33\n7+CZZ/aEXOD/7ndbOeusUdx66+SQYqyDXVcL89svqG02xahRCSQm2mhp8aMUbfWVOp7feV+Rkggc\naj9E/LTWrFtXS1NTxxRKKzth9GApPz+RxEQrE9iyZeMlQBKDXvv5x25X2O02TFNjmtb73mZTtLb6\n2wYIrMGi4HNZRYWbrCwn2dnObp3HhBgsZLqdEKLbfvnLzfztb7vD7oD4/ZqVK/dz443rh9T6mSlT\n0khOjh70BafVbU+pm5npJCPDGXJRMXZsUtidtnHjksnOjq9QoyQI6Dm///02HnxwJ8nJ9qh3joJH\n1W02RWamg6QkO4mJNinsK4aEU08dGfKzYSjsdhUIegxDoXX7gEHoQWKamupqNxUVbo47Ts5NYujp\nlyBJKXWFUkorpc6LsK1IKeVXSq0P+h7fH/0UQoTburWBF1/cF7PN2rUHeeedoVNgMzHRxjnn5Efd\nnpJix+Gwao4Ep4bubOnS0WEXGna7wZIlo7vsw6RJKRx9tFyY94Q9e5p5+uk9gJV0o/2CMFYW44wM\nRyDpxvz5uWRlxRfYCjGQ3XLL5KhlCSD0Lnk0tbUejjkmejFsIQarPg+SlFJFwNXA6hjNGrTWxwR9\n7+iTzgkhurRiRXwLdIfaQt7ly8czeXJ4+m6wRlinTk1l/PjoBV9POimbpUsLI267/PIiZsyIHgCl\npzskQUAPevHF0qApdYqxYzuKBwcHSu1tnE6DwkJrHdmYMUncfPOkvuqqEL0qJcXOb34zHcOIHASZ\nJjidVp23aPLzE/nnP4fOoJgQ7fp0TZJSygAeBq4HfttD+3QBwfNXIl/FCNHHlHq+z/aj9dIeea14\nbN8e3wLdwbyQt6KilXfeqQwUkz3jjFxSUuw8+OBMHnzQqjbfng7c5TI488xcrrlmPI2NPh54YAcr\nV+6npcWPYcDYscl85ztjueyyoqijsU6nwX33zeDhh3fy0ktl1NRY6cAdDsW8eY/0Zf4AACAASURB\nVDlcc814xoxJivhc0X3B72GPx6Sx0UdysrWOzO/vCJS0howMJ2PHJpKa6mDhwjyWLSsmI0PuIomh\n48ori8nOdnH77V+wZUtjYE1SWpqdtDQ7+fmJ1NV5qanx4HZ3TKNOSrKRne0iOdke9+eCEINJXydu\nuAlYpbX+tIvqzMlKqU+xKpe9BNyltfZHaXsrcEfPdlMIEU28yQMGY5KB5mYfd931Ff/8Z0VINrvf\n/nYLF188hmuvHc+NN05i+fIJbNvWgN+vKSpKJi3NAcBLL+1jw4ZaEhIMDMOaz9/Q4GX16gMsXJhH\nbm5C1Nd2Og2WL5/A1VcXs21bIz6fyZgxSXJB3gtsNoVpmuze3UxdnTdkm9bWyHleXiK5uS4efng2\nSln1kJKSJNeRGJrefLOc0tJWbDaFUtYt1JYWP263idvtJyPDWmPpdvvbstsZOBwdk5EkcYMYivps\nup1S6ijgG8CdXTTdDxRorWcCpwMnAzfHaH83kB703fXkfiHEIYs3ecBgSzLg9Zr813+t5803K8LS\nfbe2mjzyyG5+/estgBXQTJ2azvTpGYEA6YUXSrnzzq84eNCL3W6QlGQnIcGGUop162q5+uq1gTtE\nsTgcBkcemcb06RkSIPWS447LYufOprAACcAwwOfTlJe3MmtWFtOmpXPUUekSIIkh64orPuGxx0rw\neMy2xA0GdruBUgq322Tr1sZAMWWXy0ZSkj0kQAI4/vjBdb4XIh59uSbpZKAI2KaU2g2cADyolPpe\ncCOttVtrXdn29xrgkbbnRtTWvr79G2jopf4LIYAlSwpizk9vd+GFkdffDFRvvlnOZ5/VxmyzYkUp\nO3aETytpbvZx773bYj63rKyVJ54oOaw+ip6Rm+uiocEXs43Xa1JfHx5ECTGU7NjRyIoVe6Nut9sV\nfr+mrKwlahun02Dx4uiJbYQYrPosSNJaP6C1ztNaF2mti7ASNyzTWj8Q3E4plaOUcrT93QUsAT7r\nq34KIWIbMcLF7bcfiRHj7PHd7xYNuhTJXWXsaxcpIcUbb5TT1BRtRnCHl1/eFxiRFf3nT3/aETYS\n3pnNpvjww+o+6pEQ/eOuu76KWSjbMKx04JHuulrb4Sc/OVLueoshaUDMH1BK/Qwo01r/GTgJ+JlS\nyo/Vv3eBu/qzf0KIUAsX5pGd7eSvf93FunUdd18mTkzh298ey4IFef3Yu0MTvPDY6zVpaPDi91vJ\nE9LSHIHsT5EWKMe7aLm+3kd1tZv8/MSe6bSI286djXzwQTWtrX4++6y2be2Fgc+nMc2OgkhKddSJ\nqa5292OPhehZWms++eQgn39ei9Zw1FFpbNnS9eQbh8PA79fk57soK+s4JmbPzuTKK8cxa5ZMtRND\nU78FSVrruUF//3HQ318EXuyPPgkh4nf88SM4/vgRlJW1UF3tJjXVwbhxyf3drUNmLebX7N/fGjbN\nqry8lawsJzk5Cdjt4XcgIj0WzWBMaDHY3XTTetav71gPVlnpxu32Y7cbOJ1GW+FYK1AKToUcLS2y\nEIPNhg21/O53G9mzpznk8V27GjFN3eV73WZTPPnk8VRXewJZP/PyZLBHDG0D4k6SEGLwys9PHBJ3\nRmbOzOSxx3bT3Bw+bU5rOHDAg2nqiJXljzsuK1CcNJaioiRycqJnuBO949NPD2KzdQTwKSl23G4/\nPp8JGNjtKqzIL1h3RoUYCm6++XP8/vDztMtlo7bWi9NpxAyUCgoSSUtzkpYm0+rE8NHnxWSFEGIg\nKihIjBggBaut9TJ9enhl+TlzRlBQ0HWgGK2YrOhbo0Z1BKo+nxkoGtvZNdeM76MeCdG7oq2FzMlx\noZTC641yELS59NIxvdEtIQY0CZKEEALYsKGOESNij5JaleUrwh43DMUvfnEUycm2qM895ZRsli6V\nCgUDgdNpkJfXESj5/eEXiKefnsuyZcV92S0h+pzdbjB6dCJah67NCzZrVia33z6lj3smRP+TIEkI\nIbBS4ebkJJCfn4jLFXpqTE62M2ZMEmlpjogpwAGmTk3n0Udnc8YZuSHrjkaNSuD66yfwm98cLWtc\nBpBRoxIZOzapbU1Sx8VhVpaTG2+cyMsvn9iPvROi72Rnuxg3LpmCgkSCZ52mpzu46qpxvPfeqRix\n0pkKMUTJmiQhhKAjoUJ6uoP0dAdutx/TtB4PThcdK0lDcXEKd989jYMHPezb14LTaTBhQooERwNU\nVpaLrCwXJ544glNOGcnIkS5OOWVkf3dLiD6XkeHkrruOoqgoiS+/rCc93cHcuSMlOBLDmgRJQgiB\nlXzh7bcrAz+7XJGnzsVTWT4z00lmpixwHiwWL85n/vzc/u6GEP3GblfMnJlJdraLI45I6+/uCDEg\nyBCBEEIAF17YdVIFp9Pg3HOlsvxQkpPjYu7cnP7uhhD96rTTcsjOdvV3N4QYUIbsnaTS0lLS0oJH\nQw70W1+E6G2lpaUhP+/duxeAPXv2kJGR0R9dGnRycuC881w891xpxO02m+K6646gsbGSxvhqx4p+\n1n4ctLRU4nSGp/N2uWz8139NZf/+fX3dNSH6RPsxkJ3dQEVFc8Q2+fmJXHzx+LDPESGGivr6+kN6\nntLRcp8OUkqpGcCn/d0PIYQQQgghxIAxU2u9Lt7GQzFISgPq9u7d2+lOkmj3ox9t5IMPqqJuP/74\nLH75y+my2HwQKy0tZerUqchxIIazQzkOPv/8IDffvAGvN3JdGYfD4Pe/P5qjjpI7tGLg6+3Pgp07\nG/n+99dFrTFnGPCLX0zna18b0eOvLUS86uvrKSwsBEjXWsd9W2nITrdLS0uTi8MI1q6tYdWq5pDq\n8+Ft3GzY4JEsT4NY+3tfjgMxnB3KcfDQQ5sxzURsUUpemSY8/HAFjzwixTXFwNfbnwWPPbYTtzsh\n6vEC8Je/7OfMM4tQSgZexeAiiRuGmRdfjG/u/QsvyNxkIcTwsmVLA19+2fUg44YNdWzfLgvTxPBW\nUdHKqlXVXbbbs6eZTz452Ac9EqJnSZA0zMT7wb5zZ1Mv90QIIQaWaIWCI9m5U4IkMbzt3t2EGXlW\napjuHFtCDBQSJA0z7QUze6qdEEIMFd0578UqKizEcNCdY0CuKcRgJGf5YeaEE+JbPBlvOyGEGCpm\nzcqK62LO6TSYMUMSN4jhberUNFJSul7arhQcf7xcU4jBR4KkYeaCC0ZjdPFbV8pqJ4QQw0lWlpP5\n87suLHvGGblkZDj7oEdCDFwJCTYWL+66uPYJJ4xgzJikPuiRED1LgqRhJj8/kVtvnUKsJDP/3/83\nifHjwwsvCiHEUPeDHxxBUVH0C7ri4mRuvnlSH/ZIiIHr2muLmTo1eta8UaMSuP32KX3YIyF6zpBN\nAS6iO//8AnJzXfztb7tZt6428Pj06elcfnmRpP4Ww5pSz8fVTuulvdwT0R8yMpw88shsHn54F6++\nWkZDgw+AtDQ7ixfnc+WV40hNdfRzL4UYGJKS7Pz5zzN59NFdvPRSGTU1nrbHbSxYkMdVV40jO9vV\nz70U4tAM2WKydXV1Uh8mDhUVrdTUeMjIcJCXl9jf3RE9pLS0lMLCQuQ46D4JkoaOwz0O3G4/JSXN\nKAVjxybjdMrkCzG49OVngddrUlLSjGlqRo9OJClJxuHFwFBfX096ejpIMVnRHbm5CeTmJvR3N4QQ\nYsBxuWxMmpTa390QYlBwOAwmTJCp+mLokGExIYQQQgghhAgiQZIQQgghhBBCBJEgSQghhBBCCCGC\nSJAkhBBCCCGEEEEkSBJCCCGEEEKIIBIkCSGEEEIIIUQQCZKEEEIIIYQQIogESUIIIYQQQggRRIIk\nIYQQQgghhAgiQdIA1trqx+Mx+7sbQggxbLndch4WQ4vHY+J2+/u7G0IMePb+7oAI1dLi58UXS3nh\nhX3s2dMMwPTp6SxdOpqzzhqFUqqfeyiEEEOb12vyyitlrFhRyrZtjQBMmpTCBReMZvHifOx2GV8U\ng4vfr/n73/fx/PN72brVek9PmGC9p889Nx+HQ97TQnQmQdIA0tDgZfnydXz1VUPI4xs21LFhQx2r\nVh3gZz+bimFIoCSEEL3B4zG58cb1/Oc/NSGPb93ayC9+sZl3363kd787BqdTLirF4PHDH27g00/d\nIY9t397IL3+5mbffruAPfzgGl8vWT70TYmCSs/wActddX4UFSMHeeKOcJ58s6cMeCSHE8PKHP2wL\nC5CCrV5dw5/+tL0PeyTE4Yv1nl679iB/+MO2PuyNEIODBEkDREVFK+++W9llu+eeK8U0dR/0SAgh\nhpemJh+vvlrWZbuXXtpHS4us6RBDx6uv7qepydff3RBiQJEgaYD44IMqzDjWBpeXt/LVV/W93yEh\nhBhm1qypobm56+CnqcnPJ59EH5kXYrBpafGzevWB/u6GEAOKBEkDRHdGJWUEUwghep6ch8VwJu9p\nIUJJkDRA5Ocnxt02Ly/+tkIIIeKTn58Qd9u8vPjbCjEYdOc6RIjhQLLbDRCnnDKSjAwHtbXemO1m\nz86koKD3TmQHD3r4xz/KKS9vJSnJxvz5OUycmNprryeEEH1tz55m3nqrnPp6HyNGODnrrFHk5iZw\nzDEZjB2bRElJc8znFxcnM316Rh/1VojeN2ZMEuPHJ/Pss3vZt6+FhASDuXNzOPLItP7umhD9RoKk\nAcLpNFi2rJhf/3pL1DYOh+Kaa8b3yuubpuaPf9zOM8/swevtSAzx8MO7mD07k7vumkZWlrNXXlsI\nIfpCU5OPn/xkE//6VxU6KP/Nn/60nXPOyeeWWyazfPl4fvjDL0K2B1MKli/vnfOwEL0lVolFrTWF\nhYksWPARbnfH4uhHHtnN0Uen84tfTCM3V+6ciuFHptsNIBdeWMj110/A4Qg/m6Wm2vn1r6dzzDG9\nM3r5m99s4YknSkICpHaffHKQa6/9lOZmyXwjhBicfD6TG274jPfeqwoLgEwTXn65jNtv38j8+bnc\nfvsUXK7wj8eEBIM77jiSuXNz+qjXQvSM226bQmJieB0kl8tg5sxMVq06EBIgtfv88zquueZT6upi\nz3IRYijqlztJSqkrgEeA87XWL0XYvgi4B7ABXwCXa62HRUq3yy4r4pxz8nn55X1s3dqIYcCsWVmc\nffYoEhJ6p9Dbnj3NrFhRGrPNzp1N/P3v+7jkkrG90gchhOhN77xTyeef18Vs8+67lXz22UHOPbeA\n007L4bXX9rNxo/WcadPSOeecfFJSZAKGGHzOPHMUZ51VzGuvlbFhg/WePuqodI4/PouLLlod87ml\npS08++xeli0r7ouuCjFg9PnZXilVBFwNRDwqlVIpwF+BU7XWm5VS9wE/An7QV33sb1lZTq64Ylyf\nvd7f/74v6tSSYC+8IEGSEGJwevHFfXG3O/bYTNLSHFx88Zhe7pUQfSclxc63vjWGb32r47GHHtoZ\nV/mRv/99H1dfPQ4Va96eEENMn063U0oZwMPA9YA7SrOzgc+01pvbfr4fuCjGPl1KqbT2b0CyDHTT\nrl1NcbXbu7dZCtkKIQaleM9z8bYTYiiI9/1eVeWOq4aYEENJX69JuglYpbX+NEabMUBJ0M+7gTyl\nVLS7XrcCdUHfseeNiTBOZ3xvA5tNYRgyiiSEGHziPc/F206IoaA773eHQ44NMbz02TteKXUU8A3g\nzh7e9d1AetD36B7e/4DX0uKnsdGHjmfOXARf+9qIuNrNmRNfOyGEGGjiPX/F287jMamv98rddTGo\nxft+nzkzM66AqrnZR2OjJHkSQ0Nfrkk6GSgCtrXNaR0FPKiUytNaPxDUbg9wRtDPRcB+rXXEo05r\n7SZo6t5wmS+rteaNN8p57rlSvvjCWoSZm+tiyZLRXHjhaFJTHXHv6+yzR/HHP26jvj72ie2b3yw8\nrD4LIUR/Wbp0dJfrL51Og/PPL4i5n08/PchTT5Xw0UfVmKa1zmPhwjwuuWSMFOMUg868eTmMHOmi\nqiraCghLrM9/09S8+moZzz9fyubNDQCMHp3IkiUFLF1aGDGrnhCDQZ/dSdJaP6C1ztNaF2mti7AS\nNyzrFCABvAHMUEpNbvt5OfBMX/VzMNBa85OffMmPfrQpECABVFS4eeCBHXz3u2upqfHEvb+EBBu/\n/OX0iClv211xRRHHHy93koQQg9PEiancfPOkqPVibDbFT386lREjXFH38X//t4drr/2UDz6oDix2\nb2z08eyze7n00jV8+eWwSMIqhhC73eBXv5pGcnL0QObCC0czb17ktPd+v+Z//mcDP//5V4EACayM\nePfeu51ly9bS0CDpw8XgNCAmmCqlfqaUuhZAa90AXAW8pJTajjV97uf92b+B5vnnS1m5cn/U7bt2\nNXHHHZu6tc/jjsvi0Udnc+aZudjtHVcRRx2Vxt13T+P7359wyP0VQoiB4FvfGsMf/nAMs2ZlBh4z\nDDjllGwefHAmZ5yRG/W5GzbU8rvfbY16J6q+3sdNN63H44kjVZgQA8j06Rn87W/HsWDBqJApdZMn\np/LTn07lf/5nctTnPvroLv71r6qo27/6qoG7794cdbsQA1m/FXzQWs8N+vuPO217BXilr/s0GGit\neeaZvV22+/e/D7B7dxNFRclx73vSpFR+8YtpNDR4qa72kJxsIydHqmwLIYaOOXOymTMnmwMH3NTX\n+8jMdJCR4ezyec88s7fLUgnV1R7efruCBQvyeqi3QvSNceOS+dnPjuKWW3xUVblJTLSRmxv789/n\nM3n++a5zZb3zTiVVVW5Gjox+l1aIgWhA3EkS8du1q4k9e5rjavv++9FHd2JJTXUwblyyBEhCiCFr\nxAgX48YlxxUgQfzn01ij6kIMdMnJdoqKkrsMkAA2bqznwIGup/b7/ZoPP5TjQgw+EiQNMi0t8dcp\n6E5bIYQQkZmmxu2Obxpda6ucd8Xw0J1rjNZWmYYqBh8JkgaZvLxEjDh/a6NHS6YlIYQ4XIahyM+P\n7866nHfFcFFQEP97XY4LMRj125okYSktbeY3v9nCtm2NJCTYWLKkgEsvHRu1fVaWk5NPHtnl1I+U\nFDunnx59EbIYuPz42cQmdrELjaaQQqYzHQfhad13s5uNbMSLlyyymMUskol/HZoQIjKv1+TddysD\nGUSTk+2Ul7cACpfLID3dEbG49nnnxU4hLkQkbtysZz1llGFgMJGJTGYyRqex7P3s53M+p4UWUkll\nJjPJJDPKXrvn4EEPr7++n337WkhIsDF37kimT88IbN+6tYG3366gsdHHyJEuFi7MY8aMDNatq425\n35EjXZx4YnaP9FH0PxOTL/mSHezAxKSAAo7l2IjXKJFUUslnfEYTTaSQwkxmMoKBmT1ZgqR+9N3v\nfsKKFaX4/R2rgd98s5zbbvuCp58+npNOGhnxecuWFbNmzYGYt6+vumocCQlSm2Cw2cIWnuVZ6glN\nJfwKr/ANvsF0pgNQQw1P8AR7CU3i8Q/+wamcylEc1Wd9FmKo+eCDKu688ytqajy0tvrZt68Ft9vE\n6zWx2RQ2m6Ky0lqInpXVsabpzDNzmTQptR97LgajNazhVV6lldbAYx/yIVlkcQmXMJaxNNPM0zzN\nZkIzxb3Jm8xiFktYEvdFamdaa+6/fwdPPbUnJDvj44+XMHVqGrfccgT33beD//ynJuR5Dzywg+OO\ny8JmA3+MmXff+954bLbhUcNyqNvFLp7maQ5yMOTxV3mVxSxmNrOjPreVVp7hGTayMeTxf/JPjuZo\nLuRCnMS3RrSvyHS7fnLFFZ/w7LN7QwKkdgcOeDj33I/ZuLEuwjPhiCNS+cMfjg35cG7ncCiWLx8f\n826UGJh2sYtHeCQsQAJoookneIIv+ZJGGnmAB8ICJAAfPt5p+xJCdN/q1Qf47//+nJoaDx6PyZ49\nzXg8JkqBw2Hg82n8fo1paioqWjl40INSsGDBKH7yk6n93X0xyKxlLc/zfEiA1K6GGh7kQfawh4d4\nKCxAAtBoPuETnubpQ+7Dvfdu59FHd0dMX//FF3WceeYH/Pvf1WHbTBNWr65h8uQ00tLCx9xdLoMf\n/OAIFi/OP+S+iYFjH/t4kAfDAiSAFlp4lmf5jM8iPtePn7/y17AACaz38HrW8ziPo+kihWgfkztJ\n/aC0tJkXXoidNrO11c8tt2xg5cqTI26fOTOTlStP4u23K1izpgafTzNhQgqLF+dHDJ7EwLeSlfiJ\nPhyn0bzGa0xjWsSTVLDVrO7p7gkxLNx777ZAodjqanfIQJZS1oWf3a5ISbFjmladpSefPJ4jjpA7\nSKJ7/Ph5nddjtnHj5ime4gAHYrb7gi/YzW6KKOpWHyoqWnnqqZKo2+vqvBw86MVmM6Km8N60qZ5H\nH53Frl3NfPrpQUxTM3lyKueck09a2qHd3RIDzz/4B15iFwZeyUqO5uiwaaIb2cgudsV87mY2s5Wt\nHMERh93XniJBUj/41a82R7yD1NmqVQdobPSRkhL51+RwGJx9dh5nny01OQa7csrZze4u21VSyXu8\n12W7gTYaI8RgsGlTHVu3NgJWRrv6+sgXBD6fJjPTGZjSvG1bgwRJots2sSnizIHOvuIrRjACG7Gn\n0K9mdbeDpJdfLgsMCkRy8KCV4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mQ5ckgMERpo6kNizkR/+pEsSWQqyDBIwg3p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bLzH/9IXEGKXG/OnMLe+BpCDApHHVXIq6/uY/v2ZmprfVGVhnjx8lYq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rK9gAJR6PzYdLl9q81Nhoo0oee6yNevrOO/Doo2357+ST4eyzbV4eONDObQa2e3ZTU/h1\nzONp67Ld3Gwr8X79a3vdO+QQ+9DrilIqiYmJpftLDyAieUBVVVUVeXl53Z+Amhp7Ifjkk7ZlPp/t\nw+312otPaqq9Yd250944DhsWXhgqKIDf/tZejKJRWQk//KHta+6PTGeMvTj5B8gPGmS319JiI93V\n19tC0ZAh7mn0GznShj7WyF49xrZt2ygqKqLq2mvJW78+fIW994af/7z9+YY6q7ISLrgA1qxxv1lL\nS7PHYKQKABE7qLykJPy9kSPhkkvgoYfcg58ccQT86EfRVS6oXq81HyTqepAIjz5qK8wiGTwY7r3X\nFn7uvjt8otmWFhsVtbIyvODjn0LC57Njk/yTxjY3t82J5DdokG1VLiuzhaFBg8LTcfPNNpKl6jKR\n8oDI0x1+1pizujJpfUo0vzfob95Vqqurybf3O/nGmAhdXcJpNU48+Xxwxx3BBSSwYbZra23LTXGx\nvagMGGAnvfR67bKWlrb1x42Du+6KvoAEtibP6w2+SPm7QvgHzvovaCUltoCUn98WLrmsLDiNgTe3\n27bBL34RuQuVSl5r17ovX7cObr21a8YJFBfbOVnciNhjcPRoG7EuVFYWHHywewEJbIj6Sy+1rZ9u\nFi2yc8Eo1Vedfz6cc457RcHkyXZ+o/p6e04PLSCBzbulpW2VbX4tLbbg9MUXNh+PGGGvL/7rjH8y\nWX+BKD/fVgSmpLhfy8rKbBp27IjP91ZKqTiLqrudiExt731jzOoot/MH4BRgNDDdGLMywnrfAn6L\nDS3+CXBBLCW/hHn/fTsHTKDGxuBJM5uabGEkN9cWlPLzbevT2LF2HpiDDrLhh2P1wgt2X0VFtqDj\n726Xlmb31dJi9ztxor3QBbYWNTba90LTGFjzumOHDft6zjmxp00lp40bYckS2/oST7/9rb3BSk+3\nx6C/cC7SVmAvKYF//ct29Vm92i47+GA7oeT110fedlWV7V5XVWVbW9288w585zu2IKZUXyMCc+fa\n7nGLFtleAxkZMGOGPf8DPPBA8Dnfr6qqLWBKS0vbxOLGtBWaWlpsIWrkSJvH6ursw98N74wzbOVL\nejr85z/td6utqoJ58+Cii+L7GyilVBxEOybpxXbeM8C4KLfzDPAb4N1IK4hIDvAwcIQx5jMR+RPw\nC6CdO6cksXBh+LLQYAr+Zf6xQR6PLSjV18P3v9+5/Xq98NZbba8zM8OjFqWk2JvK7dvDg0G4tRBV\nVwcXksB+Py0k9S6vvRbfQlJDg53g1S9wPFyoRx+F++4LXvbAA+1v33+sVldHLiSB/V6XXtpxepXq\nrXJz4VvfCl/e3Bx8vQgUOseSv6BkTHCkusCJ0LOz7cNv5kyYNAkeeyy68PlvvGG75+r4JKVUkomq\nkGSMGRuPnRljFgNI+1GtTgBWGGP8k638BVhIhEKSiGQAgf12ct3W6xalpeHLQrssRFq2Y0dbbXus\namvd50RyU14ePjeFW5erSGn0+fRi1psEzncSD27ddCLZvj26ZX7GtHVL9Xrbzy/x/l5K9RbV1ZHn\nSArNu/6CUejY5fYCQ5SW2kJSe3k5UG2t7fmQm7hLt1JKuenU3a6IZIhInv8R5zSNAr4KeL0ZGCYi\nkQp0PwOqAh7b4pye6LlNjOd2E+dWyEhP71wBCWxXimg/69ZP3S09bttLS9MCUm/jNi5oT8Ryo+M2\nR0u0k0v6J6uMJN7fS6neor25kTp7DXLbfix5WfOrUioJxXTHKyIzRGQdUAfsCngk0q+A/IBH4ib9\nmTEjfJk/BGogt/kpDjmk8/vNyoKp7Q4ba3PkkdGlxy3dbt9P9WwzZ8Z3ewUF0YfhPvHE8GXt5QOR\ntuOyozle4v29lOot+vWLPAdZaCWHv7ucxxNcgIpUAMrOhm98wz6P9pq2//4ajVIplZRibRa4H7gA\nWA30B24BbohzmrZgAzv4jQFKjDGufXiMMY3GmGr/A6hxW69bHHtseI1YTk54xLnQsT4idpDtnjj1\n1I7XKSqCiy8Or0nsrjSq5JKeDscfH//tnndex+v07w9nnhm+fNYsG/UxEn/I8v79I68zrwH3aQAA\nIABJREFUaJAWkpRqzymnuC8fOLCtt0DgeEKR4F4EoeG8/Y47ru0aeMABdt6lzqZFKaUSLNZCUpox\n5gMg1RhTY4y5C4j3SP5Xgf1FZLLz+grg33HeR9fo3x9uvDG4VkzEXij8NXH+2coD3//BD2wf7j1x\n4IHw3e9Gfr+w0M6LM2BA+2mEyGmcPBnVw0SqoU1Lg+uuiy3MfLTOP99WGESSlQV//KN71820NDt3\nSqRue9nZNnJdpJrsvDw7p1c0A8aV6qsOOcRODBsqNdWG9k5JCb4G+N/zeOy1xC1/Tp8O557b9trj\nsXk5NFBQoPPPt59TSqkkFNNksiLygTHmEBF5G7gG2Ap8ZIwZE+XnHwROAoYCO4EaY8wEEbkDKDbG\nPOCsdwo2Cl4qsAb4vjHGJUyc6z4SO5ks2Dlc5s2z4VcbG+2N6gEH2IvMihV2oKrHYy9Up57auZDf\nkaxYAS+9BB9/bAfb5ufbG9aTTw6ufXdL4/772wtjaBpPOQX23Td+aVRdrnUCwTVryHvrLVi8uO3/\n/M1v2uNhbFzisUT25JM2wtXmzfZ1RoadJPa66zoOz11ebo/PN990zy9r1tj3P/jAHue5uXD00fZ7\ndUXBT/VIfXIy2Vh8/LG9XqxYYfNR//72ejF+PDz8MHz0kQ3sI2Ir8i680BagFiyAr5yhw2PHwgkn\nwDHHuFdOVFTAyy/D66/byK4itlLv5JNh2rTu/b59kE4mmxx0MtnE6uxksrEWkq4GHgUOAJ4F0oBf\nGGPuiS25XScpCkl+Pp+9Mc3IaKs1N8ZGFgpc1hW8Xntx62jwbHtpTE/XGvkeKuzC6PZ/7i4NDTb6\nYnshuyPp6FhsabHzemVmxmfQuepVtJAUpUjXC5/PzquXl9c2r55fY6PNc7GMJ6qvtxVxodtSXUYL\nScmhpxaSemq6Q3W2kBTTmcoY83vn6UIRKQAyjTGJGwOU7Dye8IuOSPRRf/ZEamp0F6JEplF1H7f/\nc3dxm7crWh0diykpeqwqtaciXS88nsiVG52JSKd5VSnVg8Qa3e5D/3NjTLMxpiZwmVJKKaWUUkr1\ndLH2uwmqanLmLtIZ4JRSSimllFK9RlSFJBH5qYjsAvYTkQr/Axtue3GXplAppZRSSimlulG0Y5Ie\nAJ4C/gpcHrC82hiT6MlklVJKKaWUUipuompJMsZUGWM2G2NOAJqAscaYr4AaEdGpspVSSimllFK9\nRqyBG84A3gcecRbtC7wQ5zQppZRSSimlVMLEGrjh58D+wC4AY8wqoINZIZVSSimllFKq54i1kNRi\njNkZsqwpXolRSimllFJKqUSLtZBUIyJDAAMgIkcBFXFPlVJKKaWUUkolSLTR7fx+CiwAxonIu8BY\n4KS4p0oppZRSSimlEiSmQpIxZpmIHAkcCgiwxBhT2SUpU0oppZRSSqkEiLUlCSAfKMR2ucsFtJCk\nlFJKKaVUDyHydKKTECaaNBlzVjekxIo1BPhcYAVwOnAmsFxEzumKhCmllFJKKaVUIsTaknQLcKAx\nZhOAiIwBXgX+Hd9kKaWUUkoppVRixBrdrs5fQAIwxmwG6uKaIqWUUkoppZRKoFgLSfNF5DYRGSki\nRSJyC/CSiOSJSF5XJFAppZRSSimlulOs3e1ucv7eErL8F9hADil7nCKllFJKKaWUSqBYQ4DH2vKk\nAnz6aTUvvvg127c30K9fKkcdNZjZsweTkiKJTppSqgf4+ut6nntuGxs37iY1VTjkkEJOPHEo2dmd\nCVSqolVX52XBgu28//5OmpsN48b14/TTRzByZHaik6aUUqqL6JW1G9TXt3DTTZ+wePGOoOULF5Yy\nfHgm9903jXHjchKUOqVUsjPGcP/9X/D441swpm35f/9bzp/+tIG77prCrFkDE5fAXmzp0p38/Oef\nUFPjbV327rs7eOyxr/jud0dx9dUTEdGKLqWU6m20Zagb/Pzn4QUkv+LiBq64YjkVFU3dnCqlVE/x\n0EOb+Ne/ggtIfrW1Xq6/fjVr11Z1f8J6uU8/rebaa1cFFZD8jIEnntjCX//6ZQJSppRSqqtpIamL\nrV1bxTvvuBeQ/HbsaOKZZ7Z1U4qUUj1Jba2Xxx77qt11mpp8PPzwpnbXUbH7xz820dTka3edxx/f\nQk1NczelSCmlVHfRQlIXe/HF4ijX+7qLU6KU6okWLtxOfX1Lh+u9++4Odu5s7IYU9Q27djWxeHF5\nh+s1Nvp47bXSbkiRUkqp7qSFpC62fXtDVOuVljbi87n0pVFK9WnRnkN8PnseUfFRXt6Ir/1GpFYl\nJfVdmxillFLdTgtJXSw7O7qo6NnZKXg8OvhXKRWsX7/o4+vk5GgsnniJ9twNsf2PlFJK9QxaSOpi\nc+YM7nAdr9fHlCl5fPFFjbYmKdUHbN1ax5o1VRQXd9wCceSRg4kmeNqECTmMGqUhqeNl5Mhs9tor\nctTRxsYW6utbaG72RXWeV0op1bNo9VcXmzNnMMOGZVJSEt5lprGxhfLyRmprvXi9hg8/3MWwYZmc\nddZIzjtvtLYsKdXLvPlmKf/851d8+ml167Jp0/pz4YVjIobwHjUqm8MPHxgxQqbf3Lmj4plUBZx7\n7mhuvXVt0LJdu5rYtauJxkbbFy8nJ5Xf/W49P/jBWKZO7Z+IZCqlegmRpztcx5izuiElsemp6e6I\ntiR1sdRUD/fdN43CwvSg5fX1LWzeXEdNjZehQzPJzLRdO0pKGvjDHzZwww2rtVVJqV7k0Uc389Of\nfhJUQAJYubKSn/xkJc89FznC5a237ttuq8Y55xRxyinD45ZWZZ100jDOPbet8Ll9ewPbtze0FpAy\nMjwMH57J0qU7ueyyj6MK9KCUUqpn6NZCkohMFJElIrJeRD4SkX1d1hkjIi0isjLgMb470xlv48fn\n8MQTh3DJJWMZNCgDYwwlJfXk5qYydmw/+vdPD/vM22+Xa1hwpXqJ9etr+MMfNkR83xj49a8/i9j9\nLj8/jYcfPojrr5/EuHH9EIGUFOHQQwu5//5pXHfdpK5Kep939dV78Yc/TGPs2Gx27bLz2WVkeBgy\nJJMxY/qRkmIvo83NhptvXsPu3eFzKimllOp5uru73YPA34wxj4jImcAjwEEu69UYY6Z1a8q6WGFh\nBpdfPp7LLx/P22+Xcd11qzv8zDPPbOM73ynqhtQppbrSf/6ztcN1fD549tlt/OhHE13fz8pK4eyz\nizj77CJ8PqPdcbvRoYcO5N//3sree+e1u15dXQvz55foeVsppXqBbmtJEpHBwIHAv5xFzwJFIjJh\nD7ebISJ5/geQu4dJ7XLLlu2Kar2NG3dTXq4hfZXq6T78sCKu62kBqftF+7/54IPo1lNKKZXcurO7\nXRFQYozxAhhjDLAFcBtt3E9EPhaR5SJyi4i0F4v1Z0BVwCPp+6i1tEQ/1sjrjXKiDqVU0vJ6o8vz\n0a6nupcxJurztp6zlVKqd0jG6HYlwAhjTJmIFABPAdcCv4mw/q+AewNe55LkBaXx4yMPwA6Un5/G\nwIEZXZwapVRXmzAhh7KyjluFoz03qO4lIowb148vv9zd4boTJuj/UKmeoLdGZFPx052FpK3AMBFJ\nNcZ4RUSwrUhbAlcyxjQCZc7zChH5BzCXCIUkZ/3Wuw+JZkKRBDvxxKH84Q9fUFfXQkNDC5WVzTQ1\n+RCx4WTz89PweIRTThlOWlrkxr7t2xu4665Pee+9nXi9hvHj+3HDDZOZObOwwzQYY3j//QpeeaWE\nnTub6N8/jRNOGMqsWQO1K49ScXbGGSNZsmRnh+udcMIQnnxyCx9+WIHXaxg3rh9nnDEyLvMf1dV5\nWbBgO++9t4OmJh+jR/fj9NNHRF0w+/jjXbz8cjFlZY3k5qZyzDFDmD17MCkpiT1fVFQ08cILX7Ny\nZSUA++yTx+mnj2Dw4Mw92u577+3gmmtW8eWXtQAUFKTj8diJY2tqvFRXN+PzQVqa0L9/GllZqXg8\ncPrpI/f4OymllEq8biskOS1Dy4HzsAEbzgC2GWOCQj45Y5d2GWOaRSQDOB1Y0V3p7A7Z2alceeV4\nrr56FVVVzUHv1dZ6KS9vZNq0fM4/f3TEbdx333puvXVtUPecL7+sZeHCUo4+egjPPz8Tj8e9gLVz\nZyNXX70qLBTxwoWlTJiQw/33T2PIkD27wVBKtTn88IHMmlXIe+9FLihNnpzLz362hrq6ltZlS5fu\n5IkntnDOOUVcc81ena4E+vDDCm68cTXV1W2R195/v4KnntrKKacM56ab9o5Y2Kmubua661axfHll\n0PI33iijqCiL++6bxujR/TqVrj313HPbuOeez2lubjsPLlmyk3/8YxNXXjmBo47q3CXumGMW8dZb\nZUHLKiqaMAZSUiAjI7gHeGVlMzk5qVx//SRGjMjq1D6VUkoll+6eJ+ky4DIRWQ/cCFwIICJ3iMjl\nzjqHAStEZBWwHNgO3NXN6exyGzfuJjs7xfXGJCPDQ1OTaQ03G+o//9nKzTeviTh+4Y03Sjn//A9d\n3/N6ffzoRyvCCkh+GzbUcsUVy2loaHF9XykVO49HuOeeb3DqqcNJTQ3O8+npHr75zYF88UVtUAHJ\nzxh48smtPPDAxk7t+4svarjmmpVBBaRA8+YV87vffe76njGGa68NLyD5bd1azxVXLKe6utn1/a70\n1ltl3H33Z0EFJD+fD/74xw0sWLA95u3Onft+WAEpUEuLnQg8kMcjZGWlsGVLXcz7U0oplZy6tZBk\njPncGDPTGLOXMeZAY8wnzvJbjDEPOM+fM8ZMMcZ8wxizrzHmR06Xul6jrKyB55//mv7905k4MYcR\nI7IYODCDwYMzGDeuH6NH98PrNfzzn5tdP3/nneswHYwhfvnlEsrKGsKWv/VWGevX17b72a++quO1\n12K/uVBKRZae7uEXv9iH+fMP4/rrJ/GDH4zlxhsns2DB4Xi9HQcGePzxr6itjX0Onkce2UxDQ/vB\nBJ599mvXSJoffljBihXuBSS/0tJGXnjh65jTtacefPDLDtd54omvYtpmU1MLzz3X8Xfx+WDgQDtm\ndNiwTCZOzGHgwAyWLNnJJ59UxbRPpZRSyam7W5IUtgDjc+5ZRIS8vDQGDcqgsDAjqBvHG2+UUV8f\nXGO5dm1Vax/59ni9ht/9bn3Y8pdeKokqjS++WBzVekqp2BQWZnD22UVcdtl4zjxzJM3NPt5/v+Px\nSg0NPhYujK3yYvdub7utIn4tLYZXXgk/N8ybF915oLvPF59+Wh1VEIWdO91b4yP51a8+o7nZvUAZ\nWDFlDHi9MGhQBv37pweN44z2N1NKKZXctJCUAKWl4S08bpqafGFd7jZs6LiA5Ld1a3jXj+3bo9t3\ntGlUSu2ZsrLG1kqTjkSbf/0qKppcu6O5ccvzpaXRNeJ39/ki1t8hWp9/XhP1upG6JOu5Uymleodk\nDAHe6/XrF/3PHrpuYWF61J/Nz08LW5aTE92+o11PKbVnYslrsebLWM41btvu16+9Kera/2xX6qr9\nFRREf34NHVvmp+dO1VtEEyI7WhpKW8VLtMdlPI45bUly1NV5Wb26ktWrK9m92/b7b2rysXZtFatW\nVVJZGVu3jfYcffSQoNcNDS3s2tVEVVUTJqBPx4EHDggr6Bx22KCoCkoi8IMfjAtbftRRg6NK41FH\nDel4JaWAbdvqWLFiV1TdQFW4UaOyg+bWMcZQX99Cfb2Xlpa2JiYRmy937Wpi1apK1q6titg1zK+g\nIJ3p0/tHlY6jjhpCdXUzq1dXsmZNFY2NLVGfB7r7fDF9ev+oCjShUegiKS6uZ8WKXZx9dhEitjtd\n6CPU0KHuEUCjPccqpZRKbn2+yquysokHH9zI/PklrZGlMjM9FBSkU13dTG2tXZaWJsyZM5jLLx9P\nUdGezVmyzz55TJ/en0WLyvn66/qgbhsej5Cfn8bo0dnMnTvK9fPnnz+a++77osN97L//gLDlp5wy\nnIcf3kRNTeQB4NnZKXz72yOi/Daqr1qyZAcPP7yJVavaBqqPG9eP888fzcknD09gynqec88dxW23\nrWXHjkZ27WpuDeIgArm5dszijBkF/PnPG3jrrbLWyJYFBemcdtpwLrlkHOnp7nVe5503usPgC5Mn\n5/LEE1t4/fVSmppswSsvL5UTThhKQUE6FRWRK4nS0oTvfKd75wZKS/Pwne+M7DDi33HHDWHZssjv\nL1tWwUMPbeLjj3dRU9PMtm31tEQR2DMry0NaWngBbMSILGbP1kKSUkr1Bn26JWnXriYuvngZTz+9\nrbWAZIzhs89qePPNMlaurMLrtTcMzc2G114r5YILPmLjxj2vMZ85s4BNm3aH9Wv3+Wzo7x07Gpkx\nw31S2Lvu2i+sNSrQ0KGZvPDCoa7v5eWlce+934jYjSYrK4Xf/GYqgwZlRPlNVF/00kvF/OQnK4MK\nSGBD299++6f86U8bInxSuTnuuKGkpXnYsaMpKMqdMXaeopKSelaurGThwtKg0P8VFU384x+bufLK\n5a2Fm1BHHDGIH/5wfMR9DxuWSXFxPfPnlwRto7ray1NPbSMz00Nennt9WlqacOedUxIyT9JFF43l\n2GMjnwcPPbSQiy8eG/H9N94o5YorlvPxx7uoqmpiw4baqKY+EIEJE8K/76BBGdx337SET66rlFIq\nPvp0Ienee9fz1VfBwQ127GhsjSjX3OwLGyBcVdXMrbeu3aP9er0+fvazNaSlCampQuD8kB6PkJ5u\nb5auv35VxG28+OIsfv/7aUyenIvHY7cxaFAGV145nhUrjmb48MitXdOnD+Df/57B9743msLCdERg\nwIA05s4dxZNPHhKxcKYU2Dxy113r2g028Mgjm1mxYlf3JaqH+7//24TXaygqyiYnJ7X1nJCR4WHo\n0Ey8Xl+73RlXrKjkkUc2R3z/4ovH8re/HcBRRw0mM9ODx2Nb/a69di8GDUqPOIcSQHFxA0ccMYiL\nLx7L4MEZeDy2lemMM0bw+OOHJKxrrscj3HXXFH7zm6kceOCA1vPp1Kn53HHHvvz+99NIS3O/xFVX\nN3PbbWvx+WzFWOh1wI0IFBamsffeuQwYkEFOTioejy1kXn75OJ544hDGjk3MpLpKKaXir892t6uo\naOKNN0qDlhlj2LUreFLEmhovzc2+oIvtunU1rFlTxZQp+Z3a95///CW1tV5E7EU9NcJ/4dlnt3H/\n/dMjbufSS8dx6aXh446iMWxYFj/+8UR+/OOJnfq86ruee+7riBMZB3r66W1Mnx7e5VMF83p9PP+8\nnZsnJyc1bOB/XZ2XpiYDGOrrvWRluZ8wnntuGxdfPDZiS8b++w8I64K7cWOt61QBof7733IWLDi8\n3RapRBCx3aDnzImti9u8ecWtc0ft3NnWeuc29sjj8f8VRo/uh4jQ1ORj/vzDYwr0oJRSqmfpsy1J\nK1dWhoXGbWz0uU7o6O+KF+jDDys6ve/XXy/teCVg165m1q2r7vR+lOoKy5ZFd+x/9FHn80hfsmnT\nbnbsiDzmZ/fuFtfnoXbsaGLTpo7nDgr00UfRtfbV1np71bko8NisqmpuZ802Pp9pvRY0NxtWrmx/\nnJdSSqmerc+2JHU0u31Xfj6WzzY2RjGKWKluFE0rUizr9XWxnA/cWjoC+cdQdsW+e9P/s7Pn78Do\no7H+1kqpnieeYdDjJZo0acj1+OizLUkTJ+aELUtP9wSND/LLyAj/mdw+H6399suLar2MDA+TJ+d2\nej9KdYVoj/09ySN9SVFRNpmZkU/Fgeef9tbLzPTEHHkzMPR4e1JShHHjes94m4kT286rWVnRhQkX\nCV432t9OKaVUz9TnWpKWLt3Jr3/9GRs21FBS0oDHIwwdmklmZgoej5CXl0ZVVTM+n6G52WCM4csv\na8nI8DBwYAY1NV7q61v44Q+XM2JEFpdcMoYNG3bzwgvF7N7tZeDAdC64YAwTJ+ayYMF2du5sZMCA\ndI4/fihPP72Fxx/fSn19C83NBo/HXnjt4GGbPo/HRozy+WDIkAyuvXY1Bx44gFmzCvnvf8tbo4lN\nmZLHzJmFPPjgRpYs2UlLi4+xY3O46qoJ1NR4WbSonIYGH0VFWZx22oiox08tX76LF18sZvv2Bvr1\nS2HOnMEce+zQiOGFVd9zxhkjefbZrztc78wzuzcsdLxUVTVx1VUrefXV7TQ2+sjNTeX73x/Nbbft\nQ0pKCl9+Wcudd65j2bIKfD7DXnvlcuONkznkEBvw5KqrlvPQQ5tpbGxBBIqKsvjPf2Zy0EGFtLQY\nFi0q59VXt1NV1UxhYTonnjiMo48ewssvl1Bb62Xz5lpnDBLO+Sk9aOLSL76oaY1Cl5OTyrBhWaSn\nezjuuKGsWFHJK6+UsHNnE/n5aRx//FBmzx6ExyOUlTVw113rePvtcpqbfRQVZXP11RMZNSqbLVvq\naGrysWtXk9N6LWRnp9C/fxqpqR6OOGIQAwdGjnhZX9/CggUlLF68g/r6FkaOtOed/fbr3LjNrtTU\n5KO0tIF166qd87yv3SAkge+tXGnPvx4PnH32Uvbfv4ChQzM59dThrlMuKKWU6rnEdNR/o4cRkTyg\nqqqqiry84Bab009fwmuvbW997fOZ1puNIUMyGT48i+ZmH59/XkNjo8/ZHkGTCwKkpwupqR5aWgyN\njT5EbC2uiGCMoanJkJoq7LVXLunpHurqvHz2WU2HXWWCvwfsu28eGRkpVFQ0UV7eyNChma2Ty5aW\n1lNc3EBqqqf1Bsr/fXJyUpkwIQcJaBabM2cwd945JWJhZ/duL9dfv9p1rJU/tO2kSdqq1VNs27aN\noqIi3PJBPPzqV+vaLSgdfHABf/zj9B4XDvmJJ77iwguXuXalyslJ5bvfLeLxx7e43lQfeeRAXn+9\nrLWAE2r27IEUFmaweXN4JLVRo7JZtKiMigr38TH+c4z/vBRq7Nh+TJ3any1bwrc9Zkw2M2YUcOed\nn7lOPjtxYo4z7UD4uCgRGD8+h5deOoxRo9xbqVatquTaa1dRWRme9iOOGMTdd0+JelLXeAvNBx99\ntJNTT11CVVUzjY0tUc2J1B4RmDo1l9TUVA4+uIB77plKv359ru5RJbFI14Lu7kaWjN2/krErXbxE\n83sn4zEQzzQF7q+6upr8/HyAfGNM1ANs+0zzwIUXfhRUQIK2cNsiQmlpA+XljVRXN9PU5GstHIUW\nkMD2zff5TOsNizE26IO/gGSMrZ1cv74GMDEXkPzbrKnxUlXVTGlpAz6fobi4ntpaLzt3NlJc3OCk\nxdeaHn+Br7bWGzaA+623yrjjjk8j7u+nP3UvIAGUlzdy5ZXLKStrcH1f9T0//elkLrpoDNnZwTe/\nqanCyScP4957v9HjCkirV1dywQUfRRxrUlPj5cEHN9Hc7H5nPX9+acQCEsDbb+/gnXfKXd97993y\niAUksOeD+nr3dIkImzfXsXKlexCG1auruPHGNTQ1uad73bpqduxocg2XnZGRgjGGrVvdQ2Rv2VLH\nj3+8wrWABLBoUTm33x75vNOdysoaOPnk91oDNexpAQns/+WTT2oAG8znhhtW7/lGlVJKJYU+UeVV\nUdHUGmI3lMcjZGSIU8hocV578HjsMp+PsNpXn4+wiRt9Plt4CmyZ8xeUOttYV1xcH1YruWNHI7t3\nB89p0tJiwvZRVdUcFrr8tde2c+ml48JqhFevruT999uPRFZZ2czTT2/jyisndOKbqN7G4xGuuGIC\n3//+GN58s4zy8kby8lKZM2cwhYU9cyLiK69c3u6Afn8e83ohJaRhJFLBKVRZWRNFReFje9qLbhfI\nn58Du+ca45/TrZGCgvDfvri4HmMMLS3h0w0YY/B6DV5vC6NHZ+PzQUOD7SaYnZ3aOgbnoYc2MmvW\nwLBtP/74V+1G3ANYuLCUSy8dx5gxiR3TdNdd66ipsefOSAXGzvD57Pk2Pz+NDz6oYPXqSqZO7R+3\n7SullEqMPlFI+uMfv3DtZhLI45HW2lCPR1r/ikCTy/2LW3cbr9eEBX6oqen8xbi52dDQ0NKaHrDd\n4vxd/NrSYgtzofsuLW1g5Mi2ApEx8NJLxWEFnZdfLokqPfPmhX9W9W39+qVyyinDE52MuGgvHHZg\nJYRbC0RzdFGkATvvUXZ226m3srIp6oqU5mYfmZnBJTR/hU1jY0vYPEper691cuyWFhM0tgmCz2Pl\n5Y2MHt0vbJ4mgDVrqtm0aXfQZKktLYYFC7aHretm3rzihM/J9uKLxa3PvZHnzu2Ur77a3Vowmjev\nWAtJSqmESsauhMmYpo70ie52W7fWR7Wev7ARKJYxW10xvCt0/+0NMA7lVjAsLQ3vMue2zM3OnU34\nfL1rDJtSft0V0rmmJrhEFdoy3B63c0zgstAxS4Gv3c5lgctCW8dDhXa3ra31us4h5/7ZxqjW60qh\nv3s8BbZAJsN3VUoptef6RCHJH+ygI/4xSMHLEjuuInT/sSTHbUyIWy2x2zI32dkpQa1aSvUm3XVs\nh7YExTNyZGqqJ+R123dyP5e1LetoDFnoeSIrKwVPlEmP9hzTlboyeETgT5sM31UppdSe6/WFpNLS\nBmbOLIiqlaegIIPBg4P79LsVnPzLQ/nHB/j5fOFjF2Lh8dgbt8Da3qyslLBtirjf4LmF7D3mmCFh\ny44+OnyZm2jXU6onGj8+eN4bf8AWY4Lzu8cDPp/PCZriw+fzxZTP8/PTg14PGpQZ9WfT0sLzub9w\nk5rqCbtBz8hIaS2EuaUxcFl7Ib5HjMhi772DoySmp3v45jcHRZXuY44ZQnV1M8uX72L58l3U1sa5\nv1sUDjtsoNNbwERduIvWkCFtv52eJ5VSqnfotVVe69dX89hjX7J06c7WwkpdXUtQyOxQ5547iszM\nFO69d33Q8rQ0T1BXFBF7gxDatSXwpsovlu5x/kh6fh4PzqBqHykpQmqqUFCQTlZWSlAXuZQUISVF\ngiJrZWWlBI17ABtSfPr08Lk8jjhiUOtcKZGkpAjnnFMU/ZdRqoe5++79OPPMJa4wNRyKAAAYAUlE\nQVR5NjBfGgMNDcG1LtG28ObmupemcnJSqK3tuOtaaEsR2Lzp9UJhYbrLJ2wI/6+/rndtKRKR1vNH\nXl7kFve5c0e5VsSce+4oFi8ub/c8N3ZsP15+uZjXXittPY9mZaVwwglD+eEPxzNggHu64+nRRzdT\nXFzfuv9YzsvRGDbMjv0sKspi9uzoCo5KKaWSW69tSfqf/1nBe+/tbL0Yjh+fQ3q6B6/X59r3fvbs\nQdxzzzf45S+ncPzxQ4PeS02V1oKVCGRkeEhJkbDayPCuepFbokKFbsvjgfT0FFJTBY9HnD7v9kZm\n+PCs1hsaj0dIS7PR+PxpTEvzhM0GP2pUNvfcMzXCvoX77psWVBsa+v1vv31f9tpL50lSvde3vz2C\nkSOz2l0ntLXYL7S1yU1BQRrTprlPOHrQQQVhrdiBUlJg5Ej3FicR23oxebJ7/hwzph/f+tbwiF2H\nhw7N5PTTR0Tc95lnjuTss90rSKZPH8BPfzo5YsvMkCEZVFY28dJLJUHn3fr6Fp577msuvngZFRXR\nRfbbEw8/vImGBl9rIJt4tiRNnGjPtUOGZHD//dO1S7JSSvUSvbYlqbExuAtMaqqHvffOZfv2Rioq\nbACC9HRbmPjhD8dzySXjWtd99tlD+ec/N/OnP21g/foafD5byNh///6UlDSwdm21s30hLy8Fr9dQ\nX+8LGrzrbxXyF5JSUiQoVLcI5OenMnRoJlu21NPQYGuR09LaanRtKF7bepSR4aGhwUdlZRMjRmTx\nne8UkZ+fyttvl/P55zaNI0dmc/TRNgTz++/vdG4K7Mz3p546vN1JDkeNyuaJJ2bw/PNfM29eMSUl\nNvz4nDmDOfvsorCuSEr1Ng8/vJEdO5pIT7fR6gILQ/7yhc9n86gN99/2XmqqrawYPDi9dd6iwMmn\nzz57JI8+OoPi4nqefnobCxaUUFXVTEFBOiedNIwzzxzJ4MGZXHrpMp54YktrWO3UVOGggwbw3HOz\nyMlJ5e671/HUU1spK2vE44FJk3L58Y8nct55oykra+CZZ7Yxf34JFRVN5OenccIJwzjrrJEMH57F\nM89s5fe//4K1a6tpaTEUFqZz+ukjuPnmfcjLS2XhwlKeeWYbn39eQ0qKcMABAzjrrJEcemh46O9A\nZ5wxkn33zeOpp7ayaFE5DQ0+RozI4tvfHsGSJTvanV5gy5Y6fv/79fzyl1M6/X+LxaBBGWRnp1BS\n0kBtrbfdkO8dycrysNdeOYwdm8Opp47g298eEfX4V6WUUslPYone1hOISB5QNW3aC6SkRJ6XY/To\nbJ599tBO7+eaa1ayePGOoGVffFET1tferXvfoYcW8vrrRwQtW7++hrlzP+hwvzk5qbz66uFhg7+V\nChRplnUV2YEHvsG6de4Tcft8vtYudikpdi61SF56aRZz5ui4lJKSek499b0Ou7alpQmvvHJ4l3S7\n8+eD9q4HtbVefD5DXl4aNTVNrF+/23U9CG6BWrp0DgcfXBjvJCsVV5GuBT0xHDOAMWfFbVs99TdQ\n0Qk8Vqqrq8nPzwfIN8a4X+hd9Nrudh356qs6yss7H6p12bLwOVX885EEcguZvXZt+P/HbXtuamu9\nfPZZTVTrKqWit2FDbcT3Am/0OwqDHzgfT1+2fHllVGN/mpsNq1dXdX2CIqir87aGYe9oUt/AOsW/\n/31TVyZLKaVUgvXZQhJ0fLPTnZ+NpduHzlWkVPy116oeS4O716v5E2I7p+1Jt7c9FRqUI1rdNa+W\nUkqpxOizhaSCgvR2Q952xC2Igdt8J25jpYuKssOWTZoUXVCEtDRhzJjI3QiVUp0zdGjkUNyB4xs7\nGpg/c6Z2wYLoz2kibcEPEiEzM6W1+3J7Ef4g+Hx+wglDI6+olFKqx+uzhaTTThve4eSJ7TnzzJFh\ny9yiU7mF7L3ssnFhyw46aABFRe1H1gI48sjBFBR0fchcpfqac88dFfE9j8fTeoMcaQoBgAED0jjv\nvNHxTlqPNGlSLvvu2/F4uIMPLnCtOOougwalM2qU3f/AgRkRI98FFpDy8tI466zIx4tSSqmer08W\nkkaNyt7jG5ljjx3CQQcFh/MtKLCRk/xSUiSsJWnKlHwuumhM2PZEhBtumNxuwW3AgDSuvHLCHqVb\nKeXuhhsmMXZs5Fba9HQhLU0i5lERui1KW09x/fWT2g1ykZ2dwlVXTezGFIX7yU/24sYbJ7eeq0eM\naL+ySgR+85v9uiFlSimlEqnXhgC/+uoiHnusjB072oIziNhayxtv3Auop7q6fo/2cfvt47jvvi94\n/fXtreMQxo0Ttm2zA4E9Hmnt456SIsyaNZBHH51Oba37APF9903jrrvGc//9X7BtW13Ie3nceONe\n5OY2U13dvEfpVr1fdXV10F8VnVdfPYC5cz9g+fJdQUEH0tI8nHrqUM4/fwz/8z8rKCkJPnfk5aVx\nyy17c8YZhfqbBxg1Srjnngn89rfr2bQpOGrcxIm53HDDRIYONV32m/m3e9BBWXz88e6g/2lBQTqX\nXDKW44/PB+Cmm8bw179+SUtLCy0thuLi8OtDRkYKt9++L2efPUj/z6pHiHwtiDx5fDKLb77rmb+B\nik7gsdLZ46Y3hgAfAWxLdDqUUkoppZRSSWOkMebraFfujYUkAYYDyRwnOxdbkBtJcqczGvpdktcE\nYEPA6972/bqa/l6xS8bfLDQfdLdk/E26U1/+/sny3ROdB3qCZPlf9SQ97TfLBYpNDAWfXtfdzvny\nUZcSE0HaBirVxDKpVTLS75LUlge+6IXfr0vp7xW7JP3Nlne8StdJ0t+k2/Tl759E3z2heaAnSKL/\nVY/RA3+zmNPYJwM3KKWUUkoppVQkWkhSSimllFJKqQBaSEqMRuB2529Pp9+l5+jt3y/e9PeKnf5m\n4fr6b9KXv39f/u49jf6vYtfrf7NeF7hBKaWUUkoppfaEtiQppZRSSimlVAAtJCmllFJKKaVUAC0k\nKaWUUkoppVQALSQppZRSSimlVAAtJKk9IiKDRGSOiAxLdFpiJSL9E50GldxEZECi09DTiMhliU6D\nSh59+XjoyddHpZQWkrqciJwV8HygiMwXkSoReVtERiUybZ0hIo+KyGDn+RzgU+DXwCoROS2hiYtd\nmYi8KCIni0ivygsiMl5E/isiG0XkXhHJDHhvaSLTlqxEZJqIrBSR5SKyr4jMB74WkS0iMjXR6UtG\nInJK6AO4PeB5n9OX815fPx562fVRqSB98dymIcC7mIgsN8bs7zx/CNgJ3AfMBQ43xnw7kemLlYis\nMsZ8w3m+CLjKGLNSRMYCzxljpic2hdETkc+BvwEXA/2BR4F/GGPWJzRhcSAirwHzgPeBq4DxwPHG\nmBoRWdGT/k/dxTmef489Fm4HbjbGPObc3FxhjDk2oQlMQiLiA5YCTQGLZ2CPO2OMmZOQhCVQX857\nff146E3Xx75MRNYbY/ZKdDqSTV88t/Wq2vMkJQHPDwZuMsZsN8bcC4xLUJr2RFbA82xjzEoAY8wm\nICUxSeq03caY3xlj9gHOBAYCy0RksYh8L8Fp21ODjTF/NsZ8bIz5HjAfeFNE8gGtGXGXZ4x5wRjz\nCLYC6TEAY8wLwOCEpix5Xez8vcYYc6Qx5khgu/O8V98Qt6Mv572+fjz0putjryYiUyM9gNxEpy9J\n9blzW2qiE9AHZIrIfjiFJWNMS8B7PfGgek1E7gduAt4QkXOBJ4DjgR0JTdkeMMYsAZaIyFXAOcCl\n2JalnirwYo0x5m4RaQLeRC8AkQRWaPy3nfeUwxjzfyLyFvB3EXkHuIueeV6Lpz6b9/R46J3Xx15q\nJbAZ93N7Yfcmpcfoc+c2bUnqelnAi84jT0RGAjglb18iE9ZJ12LT/TW2MPEYtmvFVbTVIvYUYSdH\nY8xuY8zDxpjDEpGgOFonIscHLjDG/BZ7wR6fmCQlvVIRyQMwxnzfv9AZdN2QsFQlOWPMV8CxwG7g\nHSAjsSlKuD6d9/r48dCbro+93VfAYcaYsaEPoDTRiUtSfe7cpmOSEkREsoEhTjN8j+Okfzy2NXKL\nMWZngpMUMxEpMMZUJDodXUFEMgCMMY0u740wxnzd/anqmZwKjXxjzJZEpyXZici+2LGWDyQ6LYmi\nea9NXz0eesP1sbdzWvyeNsa86/LeA8aYyxOQrKTWF89tWkhSSimllFJKqQDa3U4ppZRSSimlAmgh\nSSmllFJKKaUCaCFJKaWUUkoppQJoIUkppZRSSimlAmghqY8TkdtE5L4I720WkWlduO/TRGRGwOvZ\nIrIyhs9/S0TiFjVJRIaIyIciovOH9VHt5YcoP/93ETkywnu/FZHbnOezA0OpisgYEamMYT/fEJH5\nnU2ny/YyReRjJ5Kf6gVEpL+I3BjD+q7n+1jPy53h5LvMgNePiMhPYvj80yIyM47p+R8R+Xm8tqcS\nL175IcrPDnfmCYv0/g4RGeM8/4mIDA14L6ZrkHNdOacz6YywvW+JyN/itb2eTgtJKpFOA2Z0uFZk\nv3IecWGMKQWWAN+L1zZV32KMucQYEzoRrZvZ2AkmO+tXwK/34PNBjDEN2Dldro3XNlXC9QeivilM\nsFuBzA7XciEiBwMFxpilcUzP34CLtdKgV+m2/GCMKTbGHB7l6j8Bhna4lgsRGQGcCDzVmc+7Mca8\nDBwgIhPjtc2eTAtJSUhEskTkKRH5VERWicjCgPfOF5EPRGS5iCwWkW84yy8QkbdEZJ7zucUBNRX7\nici7zmc+FZGbO5GmoSLyH6el5RMRuTPgvc0icoeILBWRTYHbF5HJzvK1IvKciCx00noicApwvYis\nFJFLnI+kishfnO+9VkQOjJCew4FKZ+JC/7ILnW2tEpFlTu38GBGpFJFfOt//CxGZJSK/d9ZdIyJT\nAjb9JHBZrL+P6jrJkB9E5Fj/fkUkT0SaReRS5/X3ROQfzvO3ReQ05/kwEXnN2ccbgH8i6WnA5cC5\nzjF4S8B+bhfborPBySNuaRkF7GuMeSdg2Uki8pHz+6wUkUOc5UZEbnJ+o81iW29/5uSPL0RkdsCm\n/w38QETcZqBXCeT8H+8UkRUisl5Ezg147yDnWF/mvH+W89YDQK5zPCxz1r3GOU5WOn9jbn0RkeOc\n/POxcz040lk+2zmfup6/ReQyJ+3LReQXImKc5f7eAO846RrsvN5bRN50PvOciKRHSNJl2Mks/fvJ\nF9uiu8ZJhz9v3ib2GvaSs82XRWSKk0fXi8iTIuIBMMY0AQuBubH+PqrrJTI/iMgTIjLXeX6FiDSJ\nSD/n9Vsi8k0J6RkgIqeIyDoRWS0ivwlYfgswHHjKSYO/5WqYc5x+6myzIEJyLgKeNc5cPiKSLiL3\nBBz7rzrLLxCRN5xj/FMRWSIi+4jI8066FopITsB2/wNc4rK/vscYo48kewDfBl4LeF3g/J0FvAJk\nOK8PB9Y6zy8AGoG9ndc3AAud57kBn8kCVgAznNe3AfdFSMdmYJrz/DXgCOd5KvAqcFbAen9wng8E\nqoARzuuPgAud53sDDcAFzutHgJ8E7G824AUOcV5fHvg7hKTtF8BvQz67CRjmvM52HmMAA5zmLL8Y\nqAWOdF5fj51QjoDvVg/kJfo40Efy5AdnvQogAzgV2+L4H+e9x4C5zvO3A461p4FfOs9HAOXAbW77\nCThOz3BeHw98HuH3OB94JuD1XkAZMNl5nYad/BZnm1c5z49yjn1//jsL+Chk2xuBKYn+n+sj7H9u\nAo6lcc6xOAZbO74i4Lw3ENjiHG9jsBVJgdsZFPB8BvBZwOvNOOf7kM/MBlYG7Hup//wITABKnHwx\nmwjnb2CKs95Q5/XtgAn5fv0DXj8CfIA9h6cA7wHfjfDbfBl4zAL/B/wF8AR+ZyfPbQQGAAIsAt7H\nng9SgZXASQHb+V5gPtNH8jwSnB8uAv7hPH8eey040TlWd2LPv637AgY7y/dxXl/qpH+M236c43Qz\nUOi8/jfwswi/w5vAtwJe3wq8SNv1zX/sX4C9LxvlvH7MyTdDnNcvA1cGbOebwLJE/5+T4aFjL5LT\nKmwt2l+wJ/JXnOWnAt8APgio7C0QkSzn+RJjzDrn+d+AO0UkBXuD9xenlsIHFAHTsBeIDjm1JEcB\nQwL2mwNMCljtCQBjzA4R2QiMFZEaZz+POu+tE5Gw2a1DbDDGfOA8XwpcF2G9kcCGgNcnAY8ZY0qc\nfdU5aQdoMMa84Ky3DKg1bV2iPgRaa6GMMV4R2YWt3anuIK2qeyQ8Pxhj6sWOy5gFHI3t6navU/M8\nB1vYDnUUzvFrjPlaROZ18D0bgOec50uB8RHWGwmUBrw+BnjVGPOZs69m7AXRz98VYxnQD3vRBXvs\nh3ap2O5sf00HaVXd7+8AxpiNIrIYeyOzA3uTuECCGwAnYQsEoaaLyE1AIbZAM0lEsowx9VGm4Xhs\nwWhxwP58wCjneaTz9xzsMbrdef0Q0NqCGsHzAefxD4k+P3wLW1DzARhjygPeW2iM2eVscznQaIyp\ncV6vIDg/+POCSk6Jyg9vALc615J9gJuw14QW4ENjTHPIvmcAq40xnzqvHwb+2MF3e9UYs9N5vhTY\nL8J6bsf+T40xjRB27C81xmxxni8D0owdYgC2MluPfRdaSEpCTqbfB3thORr4jXNDJ8A/jTFhA0ql\n/R4yd2NPHtOdQsBzxNb/27/xGcaOXXATuLyFyMeW6WBf0W6njui/Q2PINjvaRya2NUklgSTKD284\n+/8mtm/7J8B5wK6Am792v0oH7zcapxoPe1ymRFgvlmMf2o73Fmgdf+R/rcd+z2WweWCtMebQ0DfF\n6V4a8DodWwg/0hjzkYjkYQvTGUT/PxfgdWNMWDc0seMj4nUdIIZtxZIfQrfZ3j40L/Qs3ZIfjDFb\nRKQRW7n6MbY15ybs8fNmlOnsiB77SULHJCUhERmJ7YowD1sTJ9ja7nnAeWLHJCAiHgkeszNTRCY7\nzy8B/muMacF2L9jm3BBOwtY8R80YUwv8l4BBj2Kjt7Rb02CMqca2ApznfGYScFjAKtVAZwfGria4\nJesl7G8zzNlXtohkx7pRERmCPYlt7WS6VJwlUX54AztGodIYs9t5fYfzN9L6FzlpG4Ydg+cXz2P/\nNeA4/3cVkTTpxIBzp2Z0PLbwp5LPhdB6s3c48A62q89YETnav5KITHNuAKuBLGkby5MJpGO7HwH8\nqBNpeA04WkSmBuzv4Cg+91/sMeofb3RxyPs1xC8/zAOuc1p5EZFBndzu3tjrl0pOicwPred+p2Wy\nGdt92e1asBSYGnAtusjZr188rwXzgKtEJAP02I8HLSQlp/2A90RkFbZ/7WPGmNXGDtS+AXjeeW8t\nEBj6cQnwvyKyFntD5g9AcCdwoYisxnYTeqsTaToXmOAMCPwEWwNTGMXnvgf8UETWAP+Lbdb1D2h8\nDPiO2MGVsQ4SfBmY5dzYYYxZjO3n/prz2ywCOnOCOB7bzcPXic+qrpEs+WEZ9mLmry18HRhN5NrD\nq4AZIvIptstp4H6eB6ZJSOCGKL0LjBRnMK8xZgP2huFfzu/wAcEXzmgdhh2jVNGJz6qul+J0CVsI\n/NgYs9m5QTsJ+LnYgdqfYo9pj/N/fBRYLSLLnEqrm4EPReRjoCnWBDjH2lzgQWd/67DRuTr63CfY\nfPee080tk+Auob8DXpfgwA3RegY4LuD11djWgE+cLrJ3x7g9v+OdbavklMj88Ab23P9GwOt+uBQs\nnC5vF9F2nZqIHaPk9wfgIQkO3BCt0GP/f4H1wHLn2P9njNvz02PfIW29O1RPJiIXYAeMn5botAQS\nGzFltzHGiMhYbK3KQcaYPW6pEZE/A28bY57e020FbPMd4NKAsSyqB0rW/BAvInI9gDHmnjhu89/A\nw8aY1+O1TRUfYiPBDTDGRD2XVrIRkdyA8T9XAccbY06Iw3ZzsBUiM50W3j3mdO990EQfxll1o96Q\nH+LBaS39EHut2xanbQ7EVugdaGyUxz5NW5JUVzsUWOnUoLwEXB2PApLjFmyNYVw4Xe3+qgUk1QPc\nj41UFxdiJ/JcpAUk1YV+7dSWr8UOML8yHht1uoNfDYyNx/YcRehUECrJOT1eLsNG04uX8cDlWkCy\ntCVJKaWUUkoppQJoS5JSSimllFJKBdBCklJKKaWUUkoF0EKSUkoppZRSSgXQQpJSSimllFJKBdBC\nklJKKaWUUkoF0EKSUkoppZRSSgXQQpJSSimllFJKBdBCklJKKaWUUkoF+H/33TWBR4SFvwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1fbc9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a DataFrame with training examples and feature names\n",
    "iris_df = pd.DataFrame(X_train, \n",
    "                       columns=iris_dataset.feature_names)\n",
    "\n",
    "# scatter matrix from the dataframe, color by class\n",
    "sm = pd.scatter_matrix(iris_df, c=y_train, figsize=(10, 10), \n",
    "                  marker='o', hist_kwds={'bins': 20}, s=60, \n",
    "                  alpha=.8, cmap=mglearn.cm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Fitting a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first model we'll build is called k-Nearest Neighbor, or kNN. More about that soon.  \n",
    "kNN is included in `sklearn.neighbors`, so let's build our first model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Making predictions\n",
    "Let's create a new example and ask the kNN model to classify it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n",
      "Predicted target name: ['setosa']\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
    "prediction = knn.predict(X_new)\n",
    "print(\"Prediction: {}\".format(prediction))\n",
    "print(\"Predicted target name: {}\".format(\n",
    "       iris_dataset['target_names'][prediction]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Evaluating the model\n",
    "Feeding all test examples to the model yields all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions:\\n {}\".format(y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now just count what percentage was correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Score: {:.2f}\".format(np.mean(y_pred == y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `score` function does the same thing (by default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Score: {:.2f}\".format(knn.score(X_test, y_test) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generalization, Overfitting and Underfitting\n",
    "* We __hope__ that the model can _generalize_ from the training to the test data: make accurate predictions on unseen data\n",
    "* It's easy to build a complex model that is 100% accurate on the training data, but very bad on the test data\n",
    "* Overfitting: building a model that is _too complex for the amount of data_ that we have\n",
    "    * You model peculiarities in your data (noise, biases,...)\n",
    "    * Solve by making model simpler (regularization), or getting more data\n",
    "* Underfitting: building a model that is _too simple given the complexity of the data_\n",
    "    * Use a more complex model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* There is often a sweet spot that you need to find by optimizing the choice of algorithms and hyperparameters, or using more data.  \n",
    "![model complexity image](images/01_sphx_glr_plot_underfitting_overfitting_001.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In all supervised algorithms that we will discuss, we'll cover:\n",
    "\n",
    "- How do they work\n",
    "- How to control complexity\n",
    "- Hyperparameters (user-controlled parameters)\n",
    "- Strengths and weaknesses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# k-Nearest Neighbor\n",
    "\n",
    "* Building the model consists only of storing the training dataset. \n",
    "* To make a prediction, the algorithm finds the _k_ closest data points in the training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## k-Nearest Neighbor Classification\n",
    "for k=1: return the class of the nearest neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK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\nUiA+PgpzdGFydHhyZWYKMTIwMjYKJSVFT0YK\n",
      "image/png": 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JkzUJCYncdttKkpKSmDVrFq+9toNz585w5oyNtrY2IiJk9wkhhD+Ro3Ivra2tJCaOIzl5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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1d77a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_classification(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "for k>1: do a vote and return the majority (or a confidence value for each class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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wIG9iago8PCAvTGVuZ3RoIDI0OCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+Pgpz\ndHJlYW0KeJwtUTmSA0EIy+cVekJz0++xy5H3/+kKygGDhkMgOi1xUMZPEJYr3vLIVbTh75kYwXfB\nod/KdRsWORAVSNIYVE2oXbwevQd2HGYC86Q1LIMZ6wM/Ywo3enF4TMbZ7XUZNQR712tPZlAyKxdx\nycQFU3XYyJnDT6aMC+1czw3IuRHWZRikm5XGjIQjTSFSSKHqJqkzQZAEo6tRo40cxX7pyyOdYVUj\nagz7XEvb13MTzho0OxarPDmlR1ecy8nFCysH/bzNwEVUGqs8EBJwv9tD/Zzs5Dfe0rmzxfT4XnOy\nvDAVWPHmtRuQTbX4Ny/i+D3j6/n8A6ilWxYKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAv\nTGVuZ3RoIDY4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK\n5mYGCimGXEC+qYm5Qi4XSAzEygGzDIC0JZyCiFtCNEGUglgQpWYmZhBJOAMilwYAybQV5QplbmRz\ndHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggMTU3IC9GaWx0ZXIgL0ZsYXRlRGVjb2Rl\nID4+CnN0cmVhbQp4nEWQuRFDMQhEc1VBCRKwCOqxx9F3/6kX+Uq0bwAth68lU6ofJyKm3Ndo9DB5\nDp9NJVYs2Ca2kxpyGxZBSjGYeE4xq6O3oZmH1Ou4qKq4dWaV02nLysV/82hXM5M9wjXqJ/BN6Pif\nPLSp6FugrwuUfUC1OJ1JUDF9r2KBo5x2fyKcGOA+GUeZKSNxYm4K7PcZAGa+V7jG4wXdATd5CmVu\nZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0xlbmd0aCAyMjcgL0ZpbHRlciAvRmxhdGVEZWNv\nZGUgPj4Kc3RyZWFtCnicNU87sgMhDOs5hS6QGYxtYM+zmVQv92+fZLINEv5I8vRERyZe5sgIrNnx\nthYZiBn4FlPxrz3tw4TqPbiHCOXiQphhJJw167ibp+PFv13lM9bBuw2+YpYXBLYwk/WVxZnLdsFY\nGidxTrIbY9dEbGNd6+kU1hFMKAMhne0wJcgcFSl9sqOMOTpO5InnYqrFLr/vYX3BpjGiwhxXBU/Q\nZFCWPe8moB0X9N/Vjd9JNIteAjKRYGGdJObOWU741WtHx1GLIjEnpBnkMhHSnK5iCqEJxTo7CioV\nBZfqc8rdPv9oXVtNCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0xlbmd0aCAzMzIgL0Zp\nbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVI5jiQxDMv9Cn5gAOvy8Z4eTNT7/3RJVQUF\nqmzLPORyw0QlfiyQ21Fr4tdGZqDC8K+rzIXvSNvIOohryEVcyZbCZ0Qs5DHEPMSC79v4GR75rMzJ\nswfGL9n3GVbsqQnLQsaLM7TDKo7DKsixYOsiqnt4U6TDqSTY44v/PsVzF4IWviNowC/556sjeL6k\nRdo9Ztu0Ww+WaUeVFJaD7WnOy+RL6yxXx+P5INneFTtCaleAojB3xnkujjJtZURrYWeDpMbF9ubY\nj6UEXejGZaQ4AvmZKsIDSprMbKIg/sjpIacyEKau6Uont1EVd+rJXLO5vJ1JMlv3RYrNFM7rwpn1\nd5gyq807eZYTpU5F+Bl7tgQNnePq2WuZhUa3OcErJXw2dnpy8r2aWQ/JqUhIFdO6Ck6jyBRL2Jb4\nmoqa0tTL8N+X9xl//wEz4nwBCmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0xlbmd0aCAy\nMzYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVBLbkQhDNtzilzgSSQhAc5D1VXn\n/tuxw1TtKoYYf0gP6bJVHutTYnWJ7PKlTZfKMnkVqOVP2/9RDAJu/9DIQbS3jJ1i5hLWxcIkPOU0\nIxsn1ywfjztPG2aFxsSN450uGWCfFgE1W5XNgTltOjdAupAat6qz3mRQDCLqQs0Hky6cp9GXiDme\nqGBKdya1kBtcPtWhA3FavQq5Y4uTb8QcWaHAYdBMcdZfAdaoybJZyCBJhiHOfaN7lAqNqMp5KxXC\nD5OhEfWG1aAGlbmFoqnlkvwd2gIwBbaMdekMSoGqAMHfKqd9vwEkjV1TCmVuZHN0cmVhbQplbmRv\nYmoKNDAgMCBvYmoKPDwgL0xlbmd0aCA0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0K\neJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdIAMTM5YIJ5oBZBkAaojgHriaHKw0AxugNJgplbmRzdHJl\nYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggMTcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicMza0UDCAwxRDLgAalALsCmVuZHN0cmVhbQplbmRvYmoKMTcgMCBvYmoKPDwgL0Zv\nbnREZXNjcmlwdG9yIDE2IDAgUiAvQ2hhclByb2NzIDE4IDAgUgovRm9udE1hdHJpeCBbIDAuMDAx\nIDAgMCAwLjAwMSAwIDAgXSAvTGFzdENoYXIgMjU1IC9TdWJ0eXBlIC9UeXBlMwovRW5jb2Rpbmcg\nPDwKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ2IC9wZXJpb2QgNDggL3plcm8gL29uZSAvdHdv\nIC90aHJlZSAvZm91ciAvZml2ZSA1NiAvZWlnaHQKL25pbmUgOTcgL2EgOTkgL2MgL2QgL2UgMTAz\nIC9nIDEwNSAvaSAxMDggL2wgMTEwIC9uIDExMiAvcCAxMTQgL3IgL3MgL3QgXQovVHlwZSAvRW5j\nb2RpbmcgPj4KL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvV2lkdGhzIDE1IDAg\nUiAvVHlwZSAvRm9udAovQmFzZUZvbnQgL0RlamFWdVNhbnMgL05hbWUgL0RlamFWdVNhbnMgL0Zp\ncnN0Q2hhciAwID4+CmVuZG9iagoxNiAwIG9iago8PCAvSXRhbGljQW5nbGUgMCAvRGVzY2VudCAt\nMjM2IC9Bc2NlbnQgOTI5IC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvU3RlbVYgMAovVHlwZSAvRm9u\ndERlc2NyaXB0b3IgL1hIZWlnaHQgMCAvTWF4V2lkdGggMTM0MiAvQ2FwSGVpZ2h0IDAKL0ZvbnRC\nQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRmxhZ3MgMzIgPj4KZW5kb2JqCjE1IDAgb2Jq\nClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYw\nMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAw\nIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAg\nNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2\nMzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1\nNzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYx\nMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1\nIDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUg\nNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2\nMDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNDIgNjM1IDQwMCAxMDcw\nIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUy\nMSA0MDAgMTAyMyA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUw\nMCAxMDAwIDQ3MSA2MTIgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2\nMzYgMzE4IDUwMAo0MDEgNDcxIDYxMiA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4\nNCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3\nIDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA1CjYzMCA2MTMg\nNjEzIDYxMyA2MTMgNjEzIDYxMyA5ODIgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAy\nNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5\nMiA2MzUgNTkyIF0KZW5kb2JqCjE4IDAgb2JqCjw8IC90IDE5IDAgUiAvZWlnaHQgMjAgMCBSIC9l\nIDIxIDAgUiAvciAzNiAwIFIgL2ZpdmUgMjMgMCBSIC9uIDI5IDAgUgovYSAyNSAwIFIgL2MgMjYg\nMCBSIC9mb3VyIDI3IDAgUiAvemVybyAzMSAwIFIgL2QgMzcgMCBSIC9uaW5lIDMwIDAgUgovdGhy\nZWUgMzIgMCBSIC9nIDMzIDAgUiAvdHdvIDM0IDAgUiAvaSAzNSAwIFIgL2wgMjQgMCBSIC9zIDM4\nIDAgUiAvcCAzOSAwIFIKL3BlcmlvZCA0MCAwIFIgL29uZSAyOCAwIFIgL3NwYWNlIDQxIDAgUiA+\nPgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTcgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMyA8\nPCAvY2EgMC44IC9DQSAwLjggL1R5cGUgL0V4dEdTdGF0ZSA+PgovQTIgPDwgL2NhIDEgL0NBIDEg\nL1R5cGUgL0V4dEdTdGF0ZSA+PgovQTEgPDwgL2NhIDEgL0NBIDAgL1R5cGUgL0V4dEdTdGF0ZSA+\nPiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAw\nIG9iago8PCAvTTIgMTQgMCBSIC9NMCAxMiAwIFIgL00xIDEzIDAgUiAvRGVqYVZ1U2Fucy1taW51\ncyAyMiAwIFIgPj4KZW5kb2JqCjEyIDAgb2JqCjw8IC9MZW5ndGggMTAwIC9TdWJ0eXBlIC9Gb3Jt\nIC9UeXBlIC9YT2JqZWN0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9CQm94IFsgLTUuMjU1MjgyNTgx\nNSAtNC41NDUwODQ5NzE5IDUuMjU1MjgyNTgxNSA1LjUgXSA+PgpzdHJlYW0KeJxtjjEOgDAMA/e8\nIh9olKQ1bVZGXoJQ+f9KWJCQung4nWw7X6R8UAb4pmJi7uhsggYd4EmlSQd81B80GbZVbFxUEGr9\nhS5RR3jCJvqpymmHRjZMWiqrstXq4t1JtNMDsxUl/wplbmRzdHJlYW0KZW5kb2JqCjEzIDAgb2Jq\nCjw8IC9MZW5ndGggMTMyIC9TdWJ0eXBlIC9Gb3JtIC9UeXBlIC9YT2JqZWN0IC9GaWx0ZXIgL0Zs\nYXRlRGVjb2RlCi9CQm94IFsgLTUuNSAtNS41IDUuNSA1LjUgXSA+PgpzdHJlYW0KeJxtkDsOAyEM\nRHufYi4wCNaYXdqUuUaaKFLu34ZVpBGrpUFgP54/BR/LeNo4GPhaSb61XNr52lL0/egdrKnuXtoB\nT+ERXkHdXqYs9SNAmQJ5QAEFxAuXbLIqKfxijIHMNXjnudLyXp1Tb/x3y3kALgbkchNcrIxXN8/G\n32YP+wFZyUfPCmVuZHN0cmVhbQplbmRvYmoKMTQgMCBvYmoKPDwgL0xlbmd0aCAzMyAvU3VidHlw\nZSAvRm9ybSAvVHlwZSAvWE9iamVjdCAvRmlsdGVyIC9GbGF0ZURlY29kZQovQkJveCBbIC01LjUg\nLTUuNSA1LjUgNS41IF0gPj4Kc3RyZWFtCnicM1DI4jJQ8AJiU4VcLl1TBSDK4YJSGVxcTlwAbFcG\nFAplbmRzdHJlYW0KZW5kb2JqCjIgMCBvYmoKPDwgL0NvdW50IDEgL0tpZHMgWyAxMCAwIFIgXSAv\nVHlwZSAvUGFnZXMgPj4KZW5kb2JqCjQyIDAgb2JqCjw8IC9Qcm9kdWNlciAobWF0cGxvdGxpYiBw\nZGYgYmFja2VuZCkKL0NyZWF0b3IgKG1hdHBsb3RsaWIgMi4wLjIsIGh0dHA6Ly9tYXRwbG90bGli\nLm9yZykKL0NyZWF0aW9uRGF0ZSAoRDoyMDE4MDIwNTAwMTIyMSswMicwMCcpID4+CmVuZG9iagp4\ncmVmCjAgNDMKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDE2IDAwMDAwIG4gCjAwMDAwMTIx\nNjAgMDAwMDAgbiAKMDAwMDAxMTE4MSAwMDAwMCBuIAowMDAwMDExMjEzIDAwMDAwIG4gCjAwMDAw\nMTEzNTUgMDAwMDAgbiAKMDAwMDAxMTM3NiAwMDAwMCBuIAowMDAwMDExMzk3IDAwMDAwIG4gCjAw\nMDAwMDAwNjUgMDAwMDAgbiAKMDAwMDAwMDM5NSAwMDAwMCBuIAowMDAwMDAwMjA4IDAwMDAwIG4g\nCjAwMDAwMDI5NjMgMDAwMDAgbiAKMDAwMDAxMTQ3NiAwMDAwMCBuIAowMDAwMDExNzM0IDAwMDAw\nIG4gCjAwMDAwMTE5OTcgMDAwMDAgbiAKMDAwMDAwOTg1MyAwMDAwMCBuIAowMDAwMDA5NjUzIDAw\nMDAwIG4gCjAwMDAwMDkyMTkgMDAwMDAgbiAKMDAwMDAxMDkwNiAwMDAwMCBuIAowMDAwMDAyOTg0\nIDAwMDAwIG4gCjAwMDAwMDMxODggMDAwMDAgbiAKMDAwMDAwMzY1MyAwMDAwMCBuIAowMDAwMDAz\nOTcxIDAwMDAwIG4gCjAwMDAwMDQxNDEgMDAwMDAgbiAKMDAwMDAwNDQ2MSAwMDAwMCBuIAowMDAw\nMDA0NTc4IDAwMDAwIG4gCjAwMDAwMDQ5NTUgMDAwMDAgbiAKMDAwMDAwNTI1OCAwMDAwMCBuIAow\nMDAwMDA1NDIwIDAwMDAwIG4gCjAwMDAwMDU1NzIgMDAwMDAgbiAKMDAwMDAwNTgwNiAwMDAwMCBu\nIAowMDAwMDA2MTk5IDAwMDAwIG4gCjAwMDAwMDY0ODIgMDAwMDAgbiAKMDAwMDAwNjg5MyAwMDAw\nMCBuIAowMDAwMDA3MzA0IDAwMDAwIG4gCjAwMDAwMDc2MjUgMDAwMDAgbiAKMDAwMDAwNzc2NSAw\nMDAwMCBuIAowMDAwMDA3OTk1IDAwMDAwIG4gCjAwMDAwMDgyOTUgMDAwMDAgbiAKMDAwMDAwODcw\nMCAwMDAwMCBuIAowMDAwMDA5MDA5IDAwMDAwIG4gCjAwMDAwMDkxMzAgMDAwMDAgbiAKMDAwMDAx\nMjIyMCAwMDAwMCBuIAp0cmFpbGVyCjw8IC9TaXplIDQzIC9JbmZvIDQyIDAgUiAvUm9vdCAxIDAg\nUiA+PgpzdGFydHhyZWYKMTIzNjgKJSVFT0YK\n",
      "image/png": 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k5sjsuHfeeYewsDB27NgBwJ49e1i+fLnMrPMDUVFRnDlT/cUwJyfHfrO9EML3uWOkNhJI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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1efe6f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_classification(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's build a kNN model for this dataset (called 'Forge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "X, y = mglearn.datasets.make_forge()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set accuracy: %.2f\" % clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Analysis\n",
    "We can plot the prediction for each possible input to see the _decision boundary_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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Nlz1WnMzS2HLZY8WJNo2Vyx4rTgJqbLjssuIEpcaW8z4rd8JYx7DKk9MrcBxe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\nMCBSIC9Sb290IDEgMCBSID4+CnN0YXJ0eHJlZgoxOTkwNgolJUVPRgo=\n",
      "image/png": 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/MKAF1yOHDkXL8siRvCBkiB845/SFvbfpmRMj+sLe27Rly6tCkyOtzuigIkPO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\nAFEa8FcAAAAKSURBVAAA+N7/ARm+oS0+BTA7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1f17e4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
    "\n",
    "for n_neighbors, ax in zip([1, 3, 9], axes):\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)\n",
    "    mglearn.plots.plot_2d_separator(clf, X, fill=True, eps=0.5, ax=ax, alpha=.4)\n",
    "    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)\n",
    "    ax.set_title(\"{} neighbor(s)\".format(n_neighbors))\n",
    "    ax.set_xlabel(\"feature 0\")\n",
    "    ax.set_ylabel(\"feature 1\")\n",
    "_ = axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Using few neighbors corresponds to high model complexity (left), and using many neighbors corresponds to low model complexity and smoother decision boundary (right)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can more directly measure the effect on the training and test error on a larger dataset (breast_cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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qyVyzubydSTJb90WKzRTO68KZ9XeYMqvNO3mWE6VORfgZe7YEDZ3j6tlrmYVG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\nb3QgMSAwIFIgPj4Kc3RhcnR4cmVmCjEwNTE0CiUlRU9GCg==\n",
      "image/png": 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UjJbRi4JCUyTxuDubNhXtEarLlm1j3brCSBto3jxll7msZddbW7dOo1EjBavs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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1f1e4048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, stratify=cancer.target, random_state=66)\n",
    "\n",
    "# Build a list of the training and test scores for increasing k\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "k = range(1, 11)\n",
    "\n",
    "for n_neighbors in k:\n",
    "    # build the model\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X_train, y_train)\n",
    "    # record training and test set accuracy\n",
    "    training_accuracy.append(clf.score(X_train, y_train))\n",
    "    test_accuracy.append(clf.score(X_test, y_test))\n",
    "    \n",
    "plt.plot(k, training_accuracy, label=\"training accuracy\")\n",
    "plt.plot(k, test_accuracy, label=\"test accuracy\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"n_neighbors\")\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "For small numbers of neighbors, the model is too complex, and overfits the training data. As more neighbors are considered, the model becomes simpler and the training accuracy drops, yet the test accuracy increases, up to a point. After about 8 neighbors, the model starts becoming too simple (underfits) and the test accuracy drops again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## k-Neighbors Regression\n",
    "for k=1: return the target value of the nearest neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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wIG9iago8PCAvTGVuZ3RoIDIyNyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+Pgpz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\nIFIgL1Jvb3QgMSAwIFIgPj4Kc3RhcnR4cmVmCjExNjAzCiUlRU9GCg==\n",
      "image/png": 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ehwMAAADAI7W1tUpPT5ekdMuyap3ejxEkwOeuueYar0MAAFvUKGfIE+AeGiTA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c202ca400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_regression(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "for k>1: return the _mean_ of the target values of the _k_ nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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rB/28zcBFVBqrPBAScL/bQ/2c7OQ33tK5s8X0+F5zsrwwFVjx5rUbkE21+Dcv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\ndCAxIDAgUiA+PgpzdGFydHhyZWYKMTIwNTIKJSVFT0YK\n",
      "image/png": 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IOUodthORdlggERncwIED9Q6BiEgRc5Q6bCci7bBAIjK4cePG6R0CEZEi5ih1\n2E5E2mGBRGRwjzzyiN4hEBEpYo5Sh+1EpB0WSERERERERJVYIBEZ3Ny5c/UOgYhIEXOUOmwnIu2w\nQCIyuGPHjukdAhGRIuYoddhORNphgURkcOnp6XqHQESkiDlKHbYTkXZYIBEREREREVUSUkq9Y9CM\nECIMQGFhYSHCwsL0DofIJ5SWliI4OFjvMIiInGKOUoftRFRXUVERwsPDASBcSlmk9jz2IBEZ3MMP\nP6x3CEREipij1GE7EWmHBRKRweXl5ekdAhGRIuYoddhORNphgURkcH369NE7BCIiRcxR6rCdiLTD\nOUhEBnfs2DF07NhR7zCIiJxijlKH7URUF+cgEVGjzJw5U+8QiIgUMUepw3Yi0g4LJCIiIiIiokos\nkIgMbsaMGXqHQESkiDlKHbYTkXZYIBEZXGFhod4hEBEpYo5Sh+1EpB0WSEQGt2LFCr1DICJSxByl\nDtuJSDteVyAJIYKFEP8VQuwXQvwghPhMCNFF77iIiIiIiMj/ed0y30KIYAA3AvhESimFEDMBjJNS\npqg4l8t8E7mosLDQsQQmEZHXYY5Sh+1EVJffLPMtpSyVUn4sr1Ru2wDEODtWCBEkhAhz/ABo6ak4\nifzFX/7yF71DICJSxBylDtuJSDteVyA5MRvAWoX7/gSgsNpPrqeCIvIXhw8f1jsEIiJFzFHqsJ2I\ntOPVBZIQ4jEAXWAvhJxZCCC82o/FQ6ER+Y2EhAS9QyAiUsQcpQ7biUg7XjcHyUEI8QcAdwO4WUp5\nXuU5nINE5KKTJ0+iffv2eodBROQUc5Q6bCeiuvxmDhIACCHmAhgPYLja4oiIGmfq1Kl6h0BEpIg5\nSh22E5F2AvQOoDYhhAXACwAOA/hSCAEAZVLKAboGRkRERERNYrVKZGWdQn5+KSIjg5Gc3A5ms9A7\nLKIavK5AklLmAuC/FCIPmTJlit4hEBEpYo5SxxfaKSMjF7Nn70RubknVbRZLCNLSeiM1ldPIyXt4\n5RA7IiIiIvIfGRm5GDdua43iCADy8kowbtxWZGRwIWLyHiyQiAxu2bJleodARKSIOUodb24nq1Vi\n9uydcLYumOO2OXN2wmr1zoXDyHhYIBERERGR22RlnarTc1SdlMDx4yXIyjrlwaiIlLFAIjK4N954\nQ+8QiIgUMUep483tlJ9fqulxRO7GAonI4F588UW9QyAiUsQcpY43t1NkZLCmxxG5GwskIoPbu3ev\n3iEQESlE1AR+AAAgAElEQVRijlLHm9spObkdLJYQCIU1ioUAoqNDkJzczrOBESlggURkcJ07d9Y7\nBCIiRcxR6nhzO5nNAmlpvQGgTpHk+H3Rot7cD4m8hpDOlhTxUUKIMACFhYWFCAsL0zscIp9QWFiI\n8PBwvcMgInKKOUodX2gnZ/sgRUeHYNEi7oNE7lFUVOT4dxEupSxSex4LJCKDGzVqFNatW6d3GERE\nTjFHqeMr7WS1SmRlnUJ+fikiI4ORnNyOPUfkNo0tkALcFxIRERER0RVms0BKSoTeYRDVi3OQiAzu\nnnvu0TsEIiJFzFHqsJ2ItMMCicjgvH3MOhEZG3OUOmwnIu2wQCIyuFdeeUXvEIiIFDFHqcN2ItIO\nCyQiIiIiIqJKXMWOyOCOHTuGjh076h0GEZFTzFHqsJ2I6mrsKnbsQSIyuDfffFPvEIiIFDFHqcN2\nItIOCyQig9uxY4feIRARKWKOUoftRKQdFkhEBhcVFaV3CEREipij1GE7EWmHc5CIDK60tBTBwcF6\nh0FE5BRzlDpsJ6K6OAeJiBrlrrvu0jsEIiJFzFHqsJ2ItMMCiYiIiIiIqBILJCKDGzdunN4hEBEp\nYo5Sh+1EpB0WSEQGx30ziMibMUepw3Yi0g4LJCKDe/HFF/UOgYhIEXOUOmwn0oLVKpGZWYBVq44h\nM7MAVqv/LObmigC9AyAiIiIiIn1lZORi9uydyM0tqbrNYglBWlpvpKZadIzM87jMN5HBHThwAF27\ndtU7DCIip5ij1GE7UVNkZORi3LitqF0WCGH/Mz19oE8WSVzmm4gaJT09Xe8QiIgUMUepw3bSnlGG\nm1mtErNn76xTHAGoum3OnJ1++/yd4RA7IoPbunWr3iEQESlijlKH7aQtIw03y8o6VeN51iYlcPx4\nCbKyTiElJcKDkemHPUhEBte2bVu9QyAiUsQcpQ7bSTuO4Wa1i4a8vBKMG7cVGRm5OkXmHvn5pZoe\n5w84B4mIiIiICPbhZjEx6xV7VISw9yQdOXI7zGbh4ejcIzOzAMOGbW7wuC+/HOpzPUicg0REjTJq\n1Ci9QyAiUsQcpQ7bSRuuDDfzF8nJ7WCxhFQtyFCbEEB0dAiSk9t5NjAdsUAiIiIiIoIxh5uZzQJp\nab0BoE6R5Ph90aLeftNjpgYLJCKDGzlypN4hEBEpYo5Sh+2kjcjIYE2P8xWpqRakpw9EVFRIjdst\nlhCfXeK7KbiKHZHB9ejRQ+8QiIgUMUepw3bShmO4WV5eidNlrx1zkPxxuFlqqgWjR0chK+sU8vNL\nERkZjOTkdobqOXJgDxKRwf3zn//UOwQiIkXMUeqwnbRh9OFmZrNASkoExo/viJSUCL99ng1hgURE\nREREVInDzYjLfBMZ3I8//oiePXvqHQYRkVPMUeqwnbRntUoON/NxXOabiBpl48aNeodARKSIOUod\ntpP2ONzMuFggERnc5s0Nbw5HRKQX5ih12E5E2mGBRGRwLVq00DsEIiJFzFHqsJ2ItMM5SERERERE\n5Hc4B4mIGuXOO+/UOwQiIkXMUeqwnYi0wwKJyODKysr0DoGISBFzlDpsJyLtsEAiMrjhw4frHQIR\nkSLmKHXYTkTaYYFEZHCDBw/WOwQiIkXMUeqwnYi0wwKJyOCeeuopvUMgIlLEHKUO24lIOyyQiIiI\niIiIKrFAIjK4J554Qu8QiIgUMUepw3Yi0g4LJCKD+/bbb/UOgYhIEXOUOmwnIu2wQCIyuA0bNugd\nAhGRIuYoddhORNphgURkcAEBAXqHQESkiDlKHbYTkXaElFLvGDQjhAgDUFhYWIiwsDC9wyEiIiIi\nIp0UFRUhPDwcAMKllEVqz2MPEpHBTZw4Ue8QiIgUMUepw3Yi0g4LJCKDKypS/YUKEZHHMUepw3Yi\n0g4LJCKDS05O1jsEIiJFzFHqsJ2ItMMCicjgbr/9dr1DICJSxBylDtuJSDsskIgM7tFHH9U7BCIi\nRcxR6rCdiLTDAomIiIiIiKgSCyQig5s3b57eIRARKWKOUoftRKQdFkhEBpeTk6N3CEREipij1GE7\nEWmHBRKRwa1du1bvEIiIFDFHqcN2ItIOCyQiIiIiIqJKQkqpdwyaEUKEASgsLCxEWFiY3uEQ+QSr\n1Qqz2ax3GESkM6tVIivrFPLzSxEZGYzk5HYwm4XeYTFHqcR2IqqrqKgI4eHhABAupVS9mzJ7kIgM\n7sEHH9Q7BCLSWUZGLmJi1mPYsM2YMOEbDBu2GTEx65GRkat3aMxRKrGdiLTjdQWSEOIlIcRRIYQU\nQvTWOx4if1dQUKB3CESko4yMXIwbtxW5uSU1bs/LK8G4cVt1L5KYo9RhOxFpx+sKJADpAG4A8LPe\ngRAZQf/+/fUOgYh0YrVKzJ69E85G2ztumzNnJ6xW/YbjM0ep4+52slolMjMLsGrVMWRmFuj6niBy\ntwC9A6hNSvkVAAjR8LhnIUQQgKBqN7V0U1hEfmv8+PF6h0BEOsnKOlWn56g6KYHjx0uQlXUKKSkR\nHozsCuYoddzZThkZuZg9e2eN94rFEoK0tN5ITbW47XGJ9OKNPUiu+BOAwmo/+g+WJvIxDz/8sN4h\nEJFO8vNLNT3OHZij1HFXO3n7EEwid/D1AmkhgPBqP/wag4iISKXIyGBNjyP/4gtDMIncwacLJCll\nmZSyyPEDoFjvmIh8zezZs/UOgYh0kpzcDhZLCJRGtQsBREeHIDm5nWcDq4Y5Sh13tJMrQzCJ/IlP\nF0hE1HT5+fl6h0BEOjGbBdLS7AvG1i6SHL8vWtRb1/2QmKPUcUc7+cIQTCJ38LoCSQixRAiRC/tw\nuQ1CiIN6x0Tkz1avXq13CESko9RUC9LTByIqKqTG7RZLCNLTB+o+CZ85Sh13tBOHYJJReeMqdtzp\njIiIyINSUy0YPToKWVmnkJ9fisjIYCQnt9O154j0d+7cVgCnAbSBs+/UhbAX0noOwSR9Wa3SL/OG\nkM5m3vkoIUQYgMLCwkKEhYXpHQ6RT7h06RJCQ0P1DoOIyCnmKHW0bqenn34aTz75JID+AP4AIVBj\nsQbHEExv6GUkffjC8u9FRUUIDw8HgPDK9QpU8bohdkTkWX/84x/1DoGISBFzlDpattOvfvWryuII\nOHz4PaxZ471DMEkf/r78u9cNsSMizzp+/LjeIRARKWKOUkeLdrJarQgJCUF5eTkAoLi4GC1atMA1\n14BDMKlKQ8u/C2Ff/n306CiffY+wQCIyuGuvvVbvEIiIFDFHqdPUdiouLq6anhAUFISLFy/CbDZX\n3W82C6SkRDTpMcg/uLL8u6++ZzjEjsjgHnyQ66IQkfdijlKnKe10+PDhquLotttuQ2lpaY3iiKg6\nIyz/zgKJyOBmzJihdwhERIqMlKOsVonMzAKsWnUMmZkFsFrVLaS1YwfQt28Bdu50/TG/+OILxMbG\nAgAWLFiA9evXu34RMhS1y7qfPFmq+j3sbVggEREREeksIyMXMTHrMWzYZkyY8A2GDduMmJj1qia7\nr14NnDrVF65uhfTSSy/hpptuAgCsW7cOjz/+eGNCJ4NJTm4HiyWkzubStT388A+q38PehgUSkcFx\n+AoReTMj5Kimrgj2wQc1/1Rj4sSJmD17NgDgp59+wq9//WuXYtZbY3vbqOnMZoG0tN4A0GCR5Kur\n2rFAIjK4S5cu6R0CEZEif89RDa0IBthXBFMqAI4cAXJy7H/ftw84erT+x5NSIioqCitWrAAAnD17\nFomJiY2MXh9N6W0jbaSmWpCeXnf599rUvIe9EQskIoN799139Q6BiEiRv+coV1YEc+ajjwBT5ac5\nIey/KykpKYHJZMKJEycAAOXl5WjdunWjY9eDv++/40tSUy04evR2/Otf9a+g2NB72BuxQCIiIiLS\nSVNXBFu79srfhaj5e3UnTpxAaGgoAGDAgAGw2WwICPCt3V6a2ttG2jObBdq3V7dogy+tascCicjg\n3n77bb1DINId5zN4L3/PUWpXBHN2XFERsHkzYLPZf7fZgMxMoLi45nHffPMNoqKiAACPPPIItm3b\nBtHQ5BEv1NTeNnKPpryHvRULJCKDW7Bggd4hEOmK8xm8m7/nqIZXBLMBOI2uXa117tm4EaioqHlb\nRYX9doe3334b119/PQBgxYoVeP755zWJWw9G2H/HFzX0HhYCiI4OQXJyO88G1gQskIgM7uDBg3qH\nQKQbzmfwfv6eo+pbEcz+uwDwNiyWDvjzn/9c4/4PPwRqj5ILCLDfDgCzZs3CfffdBwD47rvvMGHC\nBO2fgAf5Y0+FP2j4PQwsWtQbZrPv9FqyQCIyuPj4eL1DINIF5zP4BiPkqLorggUDaIWIiPZ47rmh\nePnlfwHog7///WMI0ReffHIS2dnAunXOe5DWrpXo2vVuLF78NYA+2LjxNIRIwvffo8ZPXp5nn2dT\n+WNPhb9QWtXOYglBevpApKZadIqscYR09j+DjxJChAEoLCwsRFhYmN7hEPmE06dP46qrrtI7DCKP\ny8wswLBhmxs87ssvhyIlJcIDEZEzRspRVqtEVtYpTJ8ehpychntBhIDTAt8+LK/h78CHDLHPYfIl\njl5foOZzdxRNvvhh3J843sP5+aWIjAxGcnI7XXuOioqKEB4eDgDhUsoiteexB4nI4KZMmaJ3CES6\n4HwG32CkHGU2C6SkROCxx4IRHNzwJpzK33HX//FOCCA4GLj//kaFqSt/66nwN4738PjxHZGSEuFT\nw+qq8631HYmIiDTC+Qzkre69F+jXDxgzBjh48MoqdVowmYCuXYGMDMDH9oetkppqwejRUV7VU0H+\nhQUSkcFNnjxZ7xCIdOGYz5CXV+L0m3gh7N9Kcz6DvoyaoxIT7fOEZs4EtFzpfNIkYPFioHJLJJ/l\n6KkgcgcOsSMyuGbNmukdApEu/HHlJX9k5BzVvDnw1lv2Aik4GDCbG3edgAD7+cuXA8uW+X5xRORu\nLJCIDO6NN97QOwQi3XA+g/djjrL3+mRnA7Gx9iFyrjCZ7Od9/7196B4RNYxD7IiIyNA4n4F8gWPI\n3aRJwJo16s8bMwZ45x32GhG5ggUSkcG99tpreodApDvOZ/BezFFXNG8OdOgAmM02WK0NdyUFBABR\nUSyOiFzFIXZEBrd48WK9QyAiUsQcdYXNBrz77mVVxRFg3zT2vfe0XQWPyAhYIBEZ3O7du/UOgYhI\nEXPUFYsXf4/z5wNdOqegANi61U0BEfkpFkhEBtepUye9QyAiUsQcZbd582bMnv0/AOU1bnesUHft\ntV84XekuIABYvdpzcRL5AyGVt2H2OUKIMACFhYWFCAsL0zscIp9QXFyMli1b6h0GEZFTzFFAVlYW\nhgwZCiAfQPuq26tv+hodXYzjx1s63Vw2IgLIz3d9BTwiX1dUVITw8HAACJdSFqk9j/9UiAzunnvu\n0TsEIiJFRs9RX3/9NYYMGQJgEKoXR4B9Rbvvv7evcHfPPfdUrXRXezlvDrMjcg0LJCIiIiIvtHXr\nVtxwww0AgNTU9wDYh9DVt+mr0uayHGZHpB4LJCKDGz9+vN4hEBEpMmqO2rZtGwYNGgQA2LTpC/zv\nf/ZNi7t0sW8aW7uXqHY7Vd9cFuBqdkSu4D5IRAbXtm1bvUMgIg+xWqXPbYhrxBz17bffYuDAgQCA\nTZs24frrhyE2Frj9dmDxYuf7GjlrJ8eQu5kzgZwcoKTE3sNERPXjIg1EBjdq1CisW7dO7zCIyM0y\nMnIxe/ZO5OaWVN1msYQgLa03UlMtOkZWP6PlqO+++w7XXXcdAGDjxo0YPnw4AHvvT32LLDTUTg2d\nT+SPuEgDEREROZWRkYtx47bWKI4AIC+vBOPGbUVGRq5OkVF12dnZVcXRhg0bqoojoOnFDYsjIvXY\ng0RkcEePHkVMTIzeYRCRm1itEjEx6+sURw5C2HuSjhy53SuH2xklR+3YsQN9+/YFAHzyyScYMWKE\nS+cbpZ2IXMEeJCJqlHfeeUfvEIjIjbKyTikWRwAgJXD8eAmysk55MCr1jJCjfvjhh6riaP369S4X\nR4Ax2onIU7hIA5HBfffdd7o8ri9OFifyRfn5pZoe52l65ShP+fHHH9G7d28AwIcffojbbrutUdfx\n93Yi8iQWSEQGFxkZ6fHH9NXJ4kS+KDIyWNPjPE2PHOUpu3fvRq9evQAAa9euxciRIxt9LX9uJyJP\n4xwkIoMrLy9Hs2bNPPZ4jsnitVOPqOw8Sk8fyCKJSEOOOUh5eSV1/t0B3j8HydM5ylP27NmD7t27\nAwA++OAD3HHHHU26nr+2E1FTcA4SETXK2LFjPfZYVqvE7Nk7nX5Ic9w2Z85OWK3+88UNkd7MZoG0\nNPsQLlGr/nH8vmhRb68sjgDP5ihP2bt3b1VxlJ6e3uTiCPDPdiJ9WK0SmZkFWLXqGDIzCwz5fzIL\nJCLyGF+fLE7kq1JTLUhPH4ioqJAat1ssIey19bCcnBwkJiYCAFavXs3ChrxKRkYuYmLWY9iwzZgw\n4RsMG7YZMTHrDbcVAOcgERncmDFjPPZYvj5ZnMiXpaZaMHp0lM8tjuLJHOVu+/fvR7du3QAA7733\nHu68807Nru1P7UT6UBoC79gvzUhfprBAIjK42NhYjz2Wr08WJ/J1ZrNASkqE3mGotmMH8Oqro9Gn\nD1C50JvPOnjwIOLj4wEAK1euxG9+8xtNr+/JXE7+p6Eh8ELYh8CPHh3l9V+qaIFD7IgM7vnnn/fY\nYyUnt4PFEgLA5vR+IYDo6BAkJ7fzWExE5L1Wrwa2b2+D1av1jqRpDh06hK5duwIA/u///g/jx4/X\n/DE8mcvJ/3AIfE0skIjIY8xmgSefjAYgULtI8oXJ4kTkWR98UPNPX3T48GF06dIFALB8+XLcc889\nOkdEVBeHwNfEAonI4J599lmPPt4f/zgUwAto167mCF9OFiei6o4cAXJy7H/ftw84etQ9j+POFbuO\nHDlSNfTtrbfewr333qvZtWvzdC4n/8Ih8DVxDhKRwa1du7Zq0rC7nThxAoWFhQC+RX5+qs9NFici\nz/noI8BkAmw2ew/zRx8BM2dq+xhabVpttco6+Sw39xg6d+4MAFi6dCkmT56sbfC1eDKXk/9xDIFv\naL80owyBZ4FEZHBff/21xx6rd+Us682bN/vcZHEio3NWBLjzS421a6/8XQj771oWSFqt2OWsyIqM\nDER+/j8AAK+//jruv/9+7QJX4MlcTv7HsV/auHFbIQRq/Lsw4hB4DrEjMrjWrVt75HFOnjyJU6fs\nkzuHDBnikcckIm14em+UoiJg82Z77xFg/zMzEygu1ub6Wm1a7Siyak9ut8/TeAQPPvgmpk6dqk3Q\nDfBULif/xf3SrhDSWXbwUUKIMACFhYWFCAsL0zscIqrGYrEgLy8PmzZtwk033aR3OESkklJPi+Nb\nZXd8cEpPB5xtEZSeDjR1X1UpJV5//WtMn57f4LFffjlUsafbapWIiVlfz8pfEtHRoThy5HbDfOtO\n/sHTvcXuVFRUhPDwcAAIl1IWqT2PPUhEBnfHHXe4/TFOnz6NvLw8AGBxRORDtOppcdWHHwIBtSYB\nBATYb3eVlBL/+9//8Ktf/QpCCJhMJkyf/qiqc+tbsauhZZEB4dFlkT2Ry8kYHEPgx4/viJSUCJ8t\njprCpTlIQojhUsrPGrqNiHyHzeZ8TyItDRgwAADw6aefuv2xiEg7ruyNomZOYV4ecPJk/cdICaxb\nB1RU1Ly9osI+Dyk7+0rvlTM2mw0HDvwPr732BL766iunxyQn90RWVoPh1rtil7cti+yJXE5kFK4u\n0vAMgNrFkLPbiMhH3HbbbW69/tmzZ3H48GEAwK233urWxyIiban9cD9sWCqAr9GlSxekpKQgJSUF\nQ4YMgcVigahWzYwfD1WFiVIBVFgI9OvX0NkmABLAleJo4sSJ+NOf/oTExEQAV4bHNWXFLm9bFtnd\nuZzISFQVSEKILgDiAIQJIar/CwwHEOqOwIjIM/r06ePW6w8ePBgA8GFjxsYQka7Uf7g/BwA4ePAg\nDh48iKVLlzo96uqr/wiz+e+wWgNg3zDaOaXp0Q1Pm7YBKENKymEsXXqwag+i2rRYscvblkV2dy4n\nMhK1c5CSAfwJQPvKPx0/EwHMc09oROQJf//739127fPnz2Pfvn0AgJEjR7rtcYjIPRxFgFKPjhBA\ndHQIKip2Q0qJsrIyfPfdd3j++ecxcuRItGjRosbxv/zyHKzWawHsB2DVNFaTSSI+3oSffgrBl1/e\np1gcOTR1xS5HkQXU7fHSY1lkd+ZyIqNR1YMkpXwLwFtCiAellEvcHBMR+YmhQ4cCADIyMnSOhIga\nw9WelsDAQCQlJSEpKQmPPPJInetdvnwZP/zwAzZu/ASvvtodeXnDYe/1afqaUZMmCSxeDIS6MK4l\nNdWC0aOjGr1il6PIcrbZ7KJFrm02S0Tew6VlvoUQAQB+D6CLlHKWECIWQLSUMtNN8bmEy3wTuW7n\nzp1VG7hqqdrSmmjsdgK+utSor8bdEH99XtQwZ5uhRkc3vQhYvhyYPh0oL5ewWl1/LwUE2H+WLAHu\nvbfRYTSZN/zbcFcuJ/JljV3m29VFGtJgn3N0feXvZwG8B+A6F69DRF4iMzPTLf+p3nzzzQCA999/\nv1HnO/tAZrGEIC3Nu7+V9dW4G+Kvz4vUaWpPizNWqxVSvovS0mcBfACgCwCz6vNNJiA2FvjgAyAh\nodFhaMKxLLKe3JXLiYzI1R6knVLK3kKIHVLKPpW37ZJS9nJbhC5gDxKR60aNGoV169Zpes0LFy6g\nZcuWABrXe5SRkYuxY7cq3r9mjXfu6K3Hhpqe4K/Pizzv0qVLWLhwIRYsWFDrnlBERW1CXt5A1dca\nOxZ45x3XhtT5M3fkciJf56mNYmus9ymE4EazRD4uJCSk4YNcNGLECADA//3f/7l8rtUqMW1adr3H\nTJuWrfnGlE2l14aa7uavz4s8Jz8/HxMmTIAQAs2bN69RHM2bNw/FxcWQ8iJSUwfW2RxWSUAAEBXF\n4qg6d+RyIqNytcD5UQhxNwAhhIgB8AoAFTsaEJG3auwQOCUXL17E119/DQC45557XD4/M7MAZ85c\nrveYM2cuIzOzoFHxuYsrG2r6En99XuReO3fuRP/+/SGEQIcOHbBq1aqq+5YsWYKKigpIKfHMM8+g\nRYsWsNmA99+vuzmskooK4L33AO6NeoXWuZzIyFwtkOYCGA7AAiAbQDMA87UOiog85ze/+Y2m1/v1\nr38NAFi2bFmjzs/MVPdBW+1xnqJ2Q021x3kLf31edIXVKpGZWYBVq44hM7OgRm9gffdVJ6XE2rVr\n0bZtWwgh0KdPH2zfvh0AEB0djY0bN8Jms0FKiWnTpsFsrjnXaMsWoMDF7zwKCoCtyiNxDUfrXE5k\nZC4t0iClLAZwf+UPEfmBkhLl3oHGXOvLL78EANx3332aXdcXqN1QU/3Gm97BX58X2dW3+AaAehfm\nKC8vx+LFizF37tw6101JScErr7yCBJWrJ6xebR82V70HybFCXYcO/8WJE3egvBywWmvev3o1ULkX\nteFpmcuJjM6lAkkIMcHJzecBfC+l/EWbkIjIk2688UbNrjVmzBgA9iE0jZWS0g4LFuxVdZw3cWyo\nmZdX4nS+jhD2D5fJyd4Vd0P89XmR8uIbeXkliouk2O/bAuAFAN/WuO+BBx7AwoULcdVVV7kUh7Ph\ndY4V6jIygI0bj+KWW4AxY4CDB68Mq3MMs/vXv+zHG52WuZzI6FxNKVMALAMwFcADAN4E8CcA3wsh\nxmkcGxF5QEpKiibXKS0txYYNGwAAU6dObUI8EWjbNrDeY9q2DdR9Sd3aHBtqAldWd3NwtqGmr/DX\n52V0ahbfcMZ+nwQwGYDAwoULUVpaCikl3njjDZeLI8D58LpJk4DvvwcSE+05KjHR/nvtvY44zO4K\nrXI5EbleIJ0D0FdKOUxKeSOAvgBOArgBwBNaBSWE6CqE2CKE2C+E2C6E6K7VtYmopieffFKT6zjG\nvy9evBii9idpF5jNAq+/nlTvMa+/nuSVH8hTUy1ITx+IqKiaq0lZLCE+vRS2vz4vI2to8Y36mQBc\nhS+//AWPPvoogoKCmhTL6tX2P81mIDjYvnnssmVXVqhz5KjmzYG33gLeftt+nGMak+N8o9MqlxOR\n6xvFxksp9zh+kVLuFULESSkPN+UDkRNLALwupXy7smfqbXAzWiKvdfny5ar9N2bMmNHk66WmWrBm\nzUA89NAO5OVdmfxvsQQjLa2PV38gd8eGmt7AX5+XUWmxqIYW13AMrwOALl3sQ+oSE+s/Z9Ik4Lrr\n7EPu9u/nMDsi0p6rBdJFIcQEKeVKoGpO0sXK+zTZBEMIEQGgH4BbKm9aA2CxEKKLlPJgrWODAFT/\n6qqlFjEQGcnjjz/e5Gs4lvN+8cUXm9R7VJ0vfyA3m4XXDQHUgr8+LyPSYlENLa5RUmKfa3T77cDi\nxc73NXKWoxxD7mbOBHJy7Ndp3rzJ4fg0LXI5Edm5WiBNAbBCCPFm5e97AUwUQjQH8KhGMUUDyJdS\nVgCAlFIKIY4B6AjgYK1j/wTgLxo9LpEh7dixAwMGDGj0+eXl5UhPTwcAzJkzR6uwAPADOZG7NLT4\nRn20XJijeXPgf/+rv/dHKUc5htzZbOw9Apqey4noCtUpRQhhAhAqpewLoAOADlLKvlLKPVLKi1LK\nT90WpbKFAMKr/XjvuBsiL/Xxxx836fzJkycDAP75z39q1ntERO6lZvGN+u7TcmGOhoqbhnIUiyO7\npuZyIrpCdVqRUtpgX8EOUspzUspzborpOIBIIUQAAAj7J66OAI45ialMSlnk+AFQ7KaYiPyWqQmf\nLsrLy7Fy5UoAwLx587QKiYg8oL7FN9asGYg1a7xjYY6m5CgjYTsRaUdIF/rWhRAZAP4gpTzsvpAA\nIUQmgLerLdLwqJSyn4rzwgAUFhYWIiwszJ0hEhGAKVOm4K233sLf/vY3PPGEZgtZEpEHWa1Sca5f\nfeI7mb0AACAASURBVPcREXm7oqIihIeHA0B4ZWeKKq4WSJ8BGADgfwAuOG6XUt6lPlRVjxMP+8p1\nbQEUAbhPSvmjivNYIBG5aNKkSVi+fLnL51VUVKBZs2YAAJvNxuF1ROQWjc1RRsN2IqqrsQWSq4s0\nvFf541ZSyhwAA939OEQEnDvXuNGyM2fOBAA88cQTLI6IyG0am6OMhu1EpB2XCiQp5ZsNH0VEvmTw\n4MEun2O1WrFkyRIAwFNPPaVxREREVzQmRxkR24lIO672IEEIkQqgN4CqDRCklJydTeSjRo8e7fI5\nDz/8MABg/vz5nBhMRG7VmBxlRGwnIu249MlGCPEvAA9U/gQBuAdA0zdCICLduLr6nNVqxcsvvwwA\n+Mc//uGOkIiIqnCFTHXYTkTacfWr35sB/BrAKSnlbAD9AERqHhURea358+cDsG8Ky94jIiIi8jeu\nrmK3XUp5nRBiJ4DrpJTlQohdUspe7gtRPa5iR+S6r776CkOGDFF1rM1mg9lsBmBfxc7xdyIid3El\nRxkZ24morsauYufq17/FQohQAFsALBdCvACg1MVrEJEXOXTokOpj//znPwMAfv/737M4IiKPcCVH\nGRnbiUg7rhZIEwFYAfwBQA4AMwBN90AiIs/64IMPVB1ns9mwcOFCAEBaWpo7QyIiqqI2Rxkd24lI\nO6pWsRNCrJJSjpdSnqh281/dFBMReaG//e1vAIAHHniAvUdERETkt1TNQRJC7JBS9vFAPE3COUhE\nrisvL0ezZs3qPUZKWbUgQ3l5OQICXN4hgIioUdTkKGI7ETnj7jlI6ldyICKfMnPmzAaPcQytu/fe\ne1kcEZFHqclRxHYi0pLaTzq9hBBnndwuAEgpZRsNYyIiD8rPz6/3fiklHn/8cQDA0qVLPRESEVGV\nhnIU2bGdiLSjtkDKAXCbOwMhIn3069ev3vuff/55AMDdd9/N4RtE5HEN5SiyYzsRaUftHKTvpZR9\nPRBPk3AOEpHrjh49ipiYGKf3VZ97VFZWhsDAQA9GRkRUf46iK9hORHW5ew6SaFRUROT1HnroIcX7\nXnrpJQDAmDFjWBwRkS7qy1F0BduJSDuqhtj5wgp2RKQtKSXmzJkDAFi1apWm17ZaJbKyTiE/vxSR\nkcFITm4Hs5nfwxAREZH+uBwVkcEprXz02muvAQBuv/12BAUFafZ4GRm5mD17J3JzS6pus1hCkJbW\nG6mpFs0eh4j8A1dnU4ftRKQdtUPsiMgP7dgB/OEPPbFzZ937ZsyYAQBIT0/X7PEyMnIxbtzWGsUR\nAOTllWDcuK3IyMjV7LGIyD+cOXNG7xB8AtuJSDsskIgMbPVq4McfI7F6dc3b33zzTQDA8OHDERwc\nrMljWa0Ss2fvhLN1YRy3zZmzE1Yrt10joiu0HuLrr9hORNphgURkYB98UPNPhwceeAAA8N///lez\nx8rKOlWn56g6KYHjx0uQlXVKs8ckIiIichULJCKDOnIEyMmx/33fPuDoUfvf33nnHQDAkCFDEBoa\nqtnj7d6dp+q4/PxSzR6TiHzfihUr9A7BJ7CdiLTDAonIoD76CKjc4ghC2H8HgEmTJgEAPv744yY/\nRklJCWbNmgUhBGbNmqjqnMhIbYb0EZF/eOyxx/QOwSewnYi0w1XsiAxq7dorfxfC/nvbtvYx7Ndf\nfz2aN2/eqOtKKfHWW2/h/vvvr3F7mzYFCAgw49Qpq9N5SELYV7NLTm7XqMclIv/0888/6x2CT2A7\nEWmHPUhEBlRUBGzeDNhs9t9tNiAzE5gwYRoAYOPGjS5f85tvvsFVV10Fk8lUozhaunQpbDYbzpw5\nhVdf7Q/AXgxV5/h90aLefr0fktUqkZlZgFWrjiEzs4ALUhCp0KNHD71D8AlsJyLtsEAiMqCNG4GK\nipq32X+/BX379kXLli1VXSc/Px/Dhw+HEALXX3991TKzM2fOxMWLFyGlxP333w9RWQGlplqQnj4Q\nUVEhNa5jsYQgPX2gX++DlJGRi5iY9Rg2bDMmTPgGw4ZtRkzMei5tTtQA7u+jDtuJSDtCOhvr4qOE\nEGEACgsLCxEWFqZ3OLqyWiWysk4hP78UkZHBSE5u59ffzJNrJk0CVq6sXSSVA1iB8+fHIDw8XPHc\nsrIyPPHEE3juuedq3D548GCsXLkSHTt2bPDxjfb+dOz/VDvdOnrO/L04JGqKUaNGYd26dXqH4fXY\nTkR1FRUVOT7ThEspi9SexzlIfigjIxezZ++ssaSyxRKCtLTe/BDm5/LygJMn6z9GSmDduro9SEAz\nmEypOHgwrM4QOCklPv30U/z5z49X3mJ/kNDQUKxfvx4pKSkuxWk2C6SkRLh0jq9qaP8nIez7P40e\nHeXXRSIREZGvYIHkZ5S+qc7LK8G4cVv5TbWfGz8eyMpq+LjaBZCDlC3Rr5/TMwD8qvIHiI3Nw/79\nkTCZOEq3Ia7s/2SUopHIFVOnTtU7BJ/AdiLSDj/d+JGGvqkG7N9Uc2K4/3rgASA4WLkAclAaWStl\n/ScKIREcDDz5ZBSLI5XU7uvE/Z+InCsvL9c7BJ/AdiLSDj/h+BFXvqkm/3TvvUB2NtC165U9jrRi\nMgFxcQLZ2fbHIXXU7uvE/Z+8G1cg1M/bb7+tdwg+ge1EpB0OsfMj/Kb6/9u7/+C66zrf4693EyVp\nabJeTDHkpLdD6WWpMBTHhdtxYptVdGbrtjREsPwqougOIinMqrjqFa93ZtHdZZvVZZYrFAoI0hsD\nrdQ7wvW2vVF6hUuJQsHyq7VNjbSyu0kpbbUnn/tHziknPTn0k+TzPd/vOd/nYyZDcvL9fs+770Pf\nPe/z+X4+H0jS/PnStm3S9ddLIf+9XLlS+u53penTw10zDdrampTJ1Gvv3kMVs/9T2hbROBHmdQJA\nujCCVEX4pBp5M2ZId9892iDV1Uk1NZO7Tm3t6Plr10pr1tAcTUZNjam7e4Gkytj/ieXIx8rP6zx+\ndD4/rzOteSmnNWvWxB1CRSBPQDg0SFUk/0l1qfknZlJra7I+qUa0Vq4cveVu7tyJ33I3bdroedu2\ncUvdVFXK/k80A2MxrzMZvvWtb8UdQkUgT0A4NEhVpNI+qUZ55G+5W758YuctXz563llnRRNX2nR0\nZLRr1xJt2rRIDzxwgTZtWqSdO5ckpjmiGSjGvM5k2LFjR9whVATyBIRDg1RlKuWTapTXjBnSaaeN\n3jLno7ZWamnhlrrQ8vs/rVgxW4sXz0rUhxU0A8WY15kMZ5xxRtwhVATyBITDIg1VqKMjo2XLWphk\njWNGRqSHHhpvc9jxHT0q/eAH0j/+Y/jV8JBMNAPFmNeZDF/96lfjDqEikCcgHN76VKkkf1KN8nvi\nCWnfvvF+U/p2qX37pK1bIwsJCUMzUIx5nclw9dVXxx1CRSBPQDg0SEAKrFs33u11f1Rdnen00x8Z\nd6W72trR85AONAPFmNcJAOlEgwRUufFvr8uqsfH3evpp6dZb/zjuSnf52+xGRsodMeJAMzA+5nXG\n78orr4w7hIpAnoBwaJCAKjf+7XVrtWPHyZo/X5o+ffqxle6OX86b2+zShWZgfElfgbDaTWe1GC/k\nCQjH3HhrulYoM2uQNDQ0NKSGhoa4wwES4YYbpO98Z/QWutpapyNHVkq6T/m/+0uXLtWGDRuOHb92\nrfRXfyX98Y9SNjt6fnd3TMEjFtmsY5EXJMbxNQrjI09AseHhYTU2NkpSo3Nu2Pc8RpCAKpa/vU6S\nzjhD+su/vEXSfbrnnntKnlO4uazEbXZpxCIvAIA0YwQJqGIHD0oXXij96Z9K3/2uNGPG6BvdbDar\nabkJRwMDA8pkim8VOnhQuv56accO6fHHR/dSAoByK1WjMBZ5AooxggSgyIwZ0s9+Jq1ZI+3a9bwk\n6fTTTz/WHEnSHXfcUfLcu+8ePZ/mCEBcStUojEWegHBokIAql++FLrroIknS+vXrx/z+l7/8pdf5\nABCHE9UojCJPQDi89QFSwDmnl156SZJ09tlnj/lda2trHCEBgBdqlB/yBITDHCQgBe6//35deeWV\nuuaaa3TXXXeN+d2bb77J8rAAEosa5Yc8AcWYgwSgpPwGgqtXry763Sc+8YlyhwMA3qhRfsgTEA4N\nElDl3njjjWPfz5w5M8ZIAAAAko8GCahyN910kySV3PvokksuKWM0ADAx1Cg/5AkIhwYJqHLf+973\nJL11m93xmpubyxkOAEwINcoPeQLCoUECqtjzz4+/91Gh7u7ucoYEABNCjfJDnoBwaJCAKpbf+2jD\nhg0xRwIAAFAZWOYbqFLOuWOjRm/39/yVV17R3LlzyxUWAEwINcoPeQKKscw3gDHuv/9+SdKnPvWp\ntz3uwQcfLEc4ADAp1Cg/5AkIhwYJqFJXXXWVpPH3Pir05JNPliMcAJgUapQf8gSEUxt3AEAlymad\n+vr2a3DwsJqb69TW1qSaGos7rGMK9z46+eST3/bYWbNmRR0OAEwaNcoPeQLCYQ4SMEG9vQPq6urX\nwMChY49lMvXq7l6gjo5MjJG95dprr9Wdd96ptWvXHhtJKiWbzaqmpqZMkQHAxFCj/JAnoBhzkIAy\n6O0dUGfn1jHNkSTt3XtInZ1b1ds7EFNkY915552SpCuuuOKExy5fvjzqcABg0qhRfsgTEA4NEuAp\nm3Xq6urXeIOu+cdWrepXNhvvqOz27dslSXPnzi259xEAAADGx7unhMtmnTZv3qcHH9ytzZv3xf7m\nO836+vYXjRwVck7as+eQ+vr2lzGqYsuWLZMkrV+/fkLHA0ASUaP8kCcgHBZpSLBKmOuSJoODh4Me\nF4WRkRG98sorkqT3vve9XueceeaZUYYEAFNCjfJDnoBwGEFKqEqZ65Imzc11QY+Lgu/eR4W+/e1v\nRxUOUo4RcIRAjfJDnoBwaJASqFLmuqRNW1uTMpl6WYnVvM2k1tZ6tbU1lTewAitXrpR04r2PgKj1\n9g5ozpyNam/fossu+4Xa27dozpyNfLgDAEg8GqQEqpS5LmlTU2Pq7l4gSUVNUv7n1asXxLYf0oED\nB459f6K9jwrdeuutUYSDFGMEHCFRo/yQJyCcRDVIZrbEzJ42syNmltqPwCthrktadXRk1NOzUC0t\n9WMez2Tq1dOzMNa5YTfeeKMkae3atRM6b+PGjVGEg5RiBByhUaP8kCcgnEQ1SJJeknSNpL+LO5A4\nVcJclzTr6Mho164l2rRpkR544AJt2rRIO3cuiX3hjLvuukuS395Hhfr6+qIIBynFCDhCo0b5IU9A\nOIlaxc4596IkmZnXbmdmdpKkkwoemhlFXOWWn+uyd++hcT+FNRsdsYhzrkva1dSYFi+eFXcYx0xl\n76OGhoYoQkJKMQKO0KhRfsgTEE7SRpAm6suShgq+EnFj+1RXbkr6XBckz0T3PiqUX/kOCIERcIRG\njfJDnoBwytogmdlWM/t9ia/WSVzybyU1FnzFvjlQqJWbkjzXBckymb2PCnV0dIQOCSlWCas9orJQ\no/yQJyCcst5i55xbGPh6RyQdyf9spf5FLpP8yk3H3xaXX7lpoo1NR0dGy5a1qK9vvwYHD6u5uU5t\nbU2MHGGMyex9VOjo0aMhw0HK5UfAOzu3ykxj6iEj4JgMapQf8gSEU+m32CVGVCs35ee6rFgxW4sX\nz+JNBYpMde+jj370oyHDARgBR1DUKD/kCQgnUYs0mNmHJK2V1DD6o3VKus45tyHeyE5sIis3JWly\nfyXKZh2jajmT3fuo0Pnnnx8qHOAYRsARCjXKD3kCwklUg+Sc+6kSMI9oMli5qTx6ewfU1dU/phnN\nZOrV3b0g0Z9KR9XUTXbvo0Lf/OY3tWFD4j+DQAVK2mqPqEzUKD/kCQgnUQ1SJWPlpuiFnuNVLlE2\ndZPd+wgAAADjMzfepJkKZWYNkoaGhobKvh9ANus0Z87GE+5dtHPnEm4xmYR8fkvdxpjU/JZq6vKT\n1afS1G3fvl1nn3225s6dq5dffnnSMW7btk3ve9/7Jn0+AESJGuWHPAHFhoeH1djYKEmNzrlh3/NY\npCEQ9i6K1kTmeCVFVAt35E1l76NCP//5z6d0PgBEiRrlhzwB4dAgBcTKTdGpxDleUTZ1U937qNDj\njz8+pfMBIErUKD/kCQiHOUiBsXJTNCpxjleUTV1+76NPf/rTEz73eCeddNKUrwEAUaFG+SFPQDjM\nQUJFqMQ5Xps371N7+5YTHrdp06IJr/SV3xT5wIEDk17eGwAAoJoxBwlVrRLneLW1NSmTqS+KN89M\nam2tV1tb04SuG2Lvo0KXXXbZlK8BAFGhRvkhT0A4NEioGJU2xyuqpi6/99G999475Rgl6Y033ghy\nHQCIAjXKD3kCwmEOEipKpc3xyjd14+2DtHr15PZBCr330aJFi4JcBwCiQI3yQ56AcGiQUHFqamzC\nc3biFLKp2759uyRp3rx5x+YhTdVHPvKRINcBgChQo/yQJyAcbrEDyiDf1K1YMVuLF8+a9IhXfu+j\nRx55JFhsX/nKV4JdCwBCo0b5IU9AODRIQIUo3Pto/vz5MUcDAABQnWiQgApx3333SZKuvfbaoNe9\n+eabg14PAEKiRvkhT0A4NEhAhbj66qslSbfddlvQ6z733HNBrwcAIVGj/JAnIBwaJKAChN77qNCj\njz4a9HoAEBI1yg95AsKhQQIqwKpVqyS9dZsdAAAAomHOubhjCMbMGiQNDQ0NqaGhIe5wgGDyS3qP\njIwEW94bAACgmg0PD6uxsVGSGp1zw77nMYIEJFz+vvKQex8V+uQnPxn8mgAQCjXKD3kCwmGjWKRC\nNuuCbNQahyj2Pir0+uuvR3JdAAiBGuWHPAHh0CCh6vX2Dqirq18DA4eOPZbJ1Ku7e4E6OjIxRnZi\nIyMjevXVVyVFt/fRwoULI7kuAIRAjfJDnoBwuMUOk5LNOm3evE8PPrhbmzfvUzabzLlsvb0D6uzc\nOqY5kqS9ew+ps3OrensHYorMT1R7HxXq7OyM7NoAMFXUKD/kCQiHRRowYZUyIpPNOs2Zs7GoOcoz\nG417584lib3dLj/n6MCBA8GX985bunSpNmzYEMm1AWCqqFF+yBNQjEUaUBaVNCLT17e/ZHMkSc5J\ne/YcUl/f/jJG5S/KvY8AAAAwPhokeMtmnbq6+jXeoGP+sVWr+hNzu93g4OGgx5VbufY+uummmyK9\nPgBMBTXKD3kCwqFBgrdKG5Fpbq4Lely5rVmzRpJ0+eWXR/o8u3fvjvT6ADAV1Cg/5AkIhwYJ3ipt\nRKatrUmZTL1KbR1kJrW21qutram8gXmIeu+jQj09PZFeHwCmghrlhzwB4dAgwVuljcjU1Ji6uxdI\nUlGTlP959eoFiVygYenSpZKi2/sIAAAA42MVO3jLrwq3d++hcechJXVVuPFW3Wttrdfq1clbda+v\nb7/27j2kK674C0kvyLmRyJ/38OHDqqtLRlMLAMejRvkhT0AxVrFLsXLtSVSpIzIdHRnt2rVEmzYt\n0gMPXKBNmxZp584liWqOensHNGfORrW3b9EVVzwp6RbNmLG2LKsC3njjjZE/BwBMFjXKD3kCwqmN\nOwBMTbn3JOroyKinZ+G4z5m0EZlCNTWmxYtnxR3GuPJLpx8/Kvfmm3Xq7Nyqnp6FkeZ17969kV0b\nAKaKGuWHPAHh0CBVsFJvrPN7EkX1xrqjI6Nly1rU17dfg4OH1dxcp7a2psSNHFWCEy2dbja6dPqy\nZS2R5fe8886L5LoAEAI1yg95AsJhDlKFys8HKrXsdlLnA2GszZv3qb19ywmP27RpUWQjYLt379bs\n2bMjuTYATBU1yg95AooxByllKm1PIowvCUunX3/99ZFdGwCmihrlhzwB4dAgVagkvLHG1FXa0ukA\nAADVjgapQvHGujokYTPb6667LrJrA8BUUaP8kCcgHBqkCpWEN9aYuiQsnT40NBTZtQFgqqhRfsgT\nEA4NUoVKwhtrhJFfOr2lpX7M45lMfeRLfEvS97///UivDwBTQY3yQ56AcFjmu4JV6p5EKMbS6QAA\nAMnAMt9VIJt1vLEuo2rL99DQUH4JTABIHGqUH/IEFGOZ7xSrqTEtXjxLK1bM1uLFsyr6zXrS9fYO\naM6cjWpv36LLLvuF2tu3aM6cjertHYg7tEn7+te/HncIAFASNcoPeQLCoUECPPX2Dqizc2vR/lN7\n9x5SZ+fWim2SXn311bhDAICSqFF+yBMQDg0S4CGbderq6td4d6TmH1u1ql/ZbOXdsnrWWWfFHQIA\nlESN8kOegHCYgwR42Lx5n9rbt5zwuE2bFmnx4llliCic1157TaeeemrcYQDAuKhRfsgTUIw5SECE\nBgcPBz0uSa699tq4QwCAkqhRfsgTEA4NEuChubku6HEAAABIJhokwENbW5MymfqiTXnzzKTW1nq1\ntTWVN7AArrnmmrhDAICSqFF+yBMQDg0S4KGmxtTdvUCSipqk/M+rVy9giXUAAIAKR4MEeOroyKin\nZ6FaWurHPJ7J1KunZ6E6OjIxRTY1a9asiTsEACiJGuWHPAHh1MYdAFBJOjoyWrasRX19+zU4eFjN\nzXVqa2ti5AgAAKBKsMw3kHIsDQsgyahRfsgTUIxlvgFMym233RZ3CABQEjXKD3kCwqFBAlLuhRde\niDsEACiJGuWHPAHh0CABKXf66afHHQIAlESN8kOegHCYgwSk3NDQUP7+XABIHGqUH/IEFGMOEoBJ\nufLKK+MOAQBKokb5IU9AODRIAAAAAJBDgwSk3OWXXx53CABQEjXKD3kCwqFBAlKOe9YBJBk1yg95\nAsKhQQJS7vbbb487BAAoiRrlhzwB4dAgAQAAAEAOy3wDKbd7927Nnj077jAAYFzUKD/kCSjGMt8A\nJuWuu+6KOwQAKIka5Yc8AeHQIAEp98wzz8QdAgCURI3yQ56AcGiQgJRraWmJOwQAKIka5Yc8AeEk\nag6Smd0g6TOSXO7r2865+ydwPnOQgAk6fPiw6urq4g4DAMZFjfJDnoBi1TIHabukDzjnzpG0RNJq\nM5sbc0xAVbvkkkviDgEASqJG+SFPQDi1cQdQyDn304Lv95jZ7yS1SnplvOPN7CRJJxU8NDPaCAEA\nAABUs6SNIB1jZh+W9C5JT73NYV+WNFTwNVCG0ICq0tnZGXcIAFASNcoPeQLCKWuDZGZbzez3Jb5a\nC447R9Ldki51zh18m0v+raTGgq9MpH8AoAqxbwaAJKNG+SFPQDhlbZCccwudc+8u8bVHksxsvqRH\nJV3jnPvZCa53xDk3nP+SdKAMfwxUsWzWafPmfXrwwd3avHmfstnkLGISldtuuy3uEACgJGqUH/IE\nhJOoOUhmdpakH0v6jHPu8bjjQbr09g6oq6tfAwOHjj2WydSru3uBOjoYnAQAAEiDpC3z/bik90v6\nTcHDX3LO/cTzfJb5xqT09g6os3Orjv/rYDb6356ehVXbJL300kuaN29e3GEAwLioUX7IE1CsKpb5\nds5d6Jx7l3NuQcGXV3METFY269TV1V/UHEk69tiqVf1Ve7tdT09P3CEAQEnUKD/kCQgnUQ0SEIe+\nvv1jbqs7nnPSnj2H1Ne3v4xRlc/WrVvjDgEASqJG+SFPQDg0SEi9wcHDQY+rNKecckrcIQBASdQo\nP+QJCCdRc5CmijlImIzNm/epvX3LCY/btGmRFi+eVYaIAAAAMFVVMQcJiENbW5MymfpjCzIcz0xq\nba1XW1tTeQMrk6VLl8YdAgCURI3yQ56AcGiQkHo1Nabu7gWSVNQk5X9evXqBampKdFAAAACoGjRI\ngKSOjox6ehaqpaV+zOOZTH1VL/EtSR/72MfiDgEASqJG+SFPQDiJ2igWiFNHR0bLlrWor2+/BgcP\nq7m5Tm1tTVU/cnT22WfHHQIAlESN8kOegHAYQQIK1NSYFi+epRUrZmvx4llV3xxJ0q233hp3CABQ\nEjXKD3kCwqFBAgAAAIAclvkGUu7ZZ5/VOeecE3cYADAuapQf8gQUY5lvAJPy2GOPxR0CAJREjfJD\nnoBwaJCAlNuy5cSb5AJAXKhRfsgTEA4NEpByJ598ctwhAEBJ1Cg/5AkIhzlIAAAAAKoOc5AATMrH\nP/7xuEMAgJKoUX7IExAODRKQckeOHIk7BAAoiRrlhzwB4dAgASl34YUXxh0CAJREjfJDnoBwaJCA\nlPvABz4QdwgAUBI1yg95AsKhQQJS7pZbbok7BAAoiRrlhzwB4dAgAQAAAEAODRKQcl/72tfiDgEA\nSqJG+SFPQDg0SEDKPfnkk3GHAAAlUaP8kCcgHBokIOV+8pOfxB0CAJREjfJDnoBwaJCAlKutrY07\nBAAoiRrlhzwB4ZhzLu4YgjGzBklDQ0NDamhoiDscJEg269TXt1+Dg4fV3FyntrYm1dRY3GEBAAAg\nIsPDw2psbJSkRufcsO95jCCh6vX2DmjOnI1qb9+iyy77hdrbt2jOnI3q7R2IO7REuOKKK+IOAQBK\nokb5IU9AODRIqGq9vQPq7NyqgYFDYx7fu/eQOju30iRp9NMVAEgqapQf8gSEQ4OEqpXNOnV19Wu8\nu0jzj61a1a9stnpuM52Mtra2uEMAgJKoUX7IExAODRKqVl/f/qKRo0LOSXv2HFJf3/4yRpU8S5Ys\niTsEACiJGuWHPAHh0CChag0OHg56XLW6+eab4w4BAEqiRvkhT0A4NEioWs3NdUGPAwAAQPWjQULV\namtrUiZTLyuxmreZ1Npar7a2pvIGljBf/OIX4w4BAEqiRvkhT0A4NEioWjU1pu7uBZJU1CTlf169\nekHq90PasWNH3CEAQEnUKD/kCQiHBglVraMjo56ehWppqR/zeCZTr56eheroyMQUWXKsX78+7hAA\noCRqlB/yBIRTG3cAQNQ6OjJatqxFfX37NTh4WM3NdWpra0r9yBEAAACKmRtvk5gKZWYNkoaGhobU\n0NAQdzhARchms6qpqYk7DAAYFzXKD3kCig0PD6uxsVGSGp1z3rspc4sdkHKf/exn4w4BAEqiQmRh\nLAAADCxJREFURvkhT0A4NEhAyu3bty/uEACgJGqUH/IEhEODBKTc+eefH3cIAFASNcoPeQLCoUEC\nUm7FihVxhwAAJVGj/JAnIBwaJCDlbrzxxrhDAICSqFF+yBMQDg0SAAAAAOTQIAEp19XVFXcIAFAS\nNcoPeQLCoUECUm5wcDDuEACgJGqUH/IEhEODBKTcunXr4g4BAEqiRvkhT0A4NEgAAAAAkGPOubhj\nCMbMGiQNDQ0NqaGhIe5wgIrw5ptvavr06XGHAQDjokb5IU9AseHhYTU2NkpSo3Nu2Pc8RpCAlPvC\nF74QdwgAUBI1yg95AsKhQQJSbs+ePXGHAAAlUaP8kCcgHBokIOXOPffcuEMAgJKoUX7IExAOc5CA\nlBsYGFAmk4k7DAAYFzXKD3kCijEHCcCkXHfddXGHAAAlUaP8kCcgHBokAAAAAMihQQJS7JnBZzTQ\nPqD+3/XHHQoAFKFG+SFPQFg0SECKrdu+Ts8MP6N129mBHUDyUKP8kCcgLBokIMUe/vXDo/994eGY\nIwGAYtQoP+QJCIsGCUipnf+2Uzte3yFJ+vXrv9auf98Vb0AAUIAa5Yc8AeHRIAEp9eiLj2qajZYA\nk+nRFx+NOSIAeAs1yg95AsKjQQJSav2O9ce+NzOt//X6tzkaAMqLGuWHPAHh0SABKTR8ZFhbfrNF\nI25EkjTiRrT5N5t14MiBmCMDAGqUL/IERIMGCUihx155TEdHjo557OjIUT32ymMxRQQAb6FG+SFP\nQDRokIAU+tGOH6l2Wu2Yx2qn1epHL/4opogA4C3UKD/kCYhG7YkPAVAp9g7v1WsHX3vbY5xz2vDi\nhnE/dVy/Y72e/u3TMrO3vcapM05VS0PLlOMFkC7UKD/kCYiXOefijiEYM2uQNDQ0NKSGhoa4wwHK\n7oN3f1B9u/tOeJzJ5FT8d7/U40XPM/uD2vLJLZOKEUB6UaP8kCcgjOHhYTU2NkpSo3Nu2Pe8RN1i\nZ2afM7NnzazfzJ4zsxvijgmoJJ9+36dVV1sn09t/aljqH84T/YNqMtXV1ulT7/vUpGMEkF7UKD/k\nCYhXokaQzKzROTeU+75B0nOSljnnnvE8nxEkpN7z+5/X8oeW6+V/ffnYykYhTLNpmvcf5qn30l7N\nb5of7LoA0oUa5Yc8AVNXFSNI+eYoZ4akd7zd8WZ2kpk15L8kzYw0QKACzG+ar22f2aarzr0q6HVX\nnrtS2z67jX9QAUwJNcoPeQLik6gGSZLMrNPMtkvaJenvTzB69GVJQwVfA9FHCCTfjHfO0N3L7tY9\ny+5RXW2daqxmUtepnVaruto6rb1ordYsW6Pp75geOFIAaUSN8kOegHiU9RY7M9sqaV6JX5/nnNtT\ncOwcSQ9L+oRzbkeJ650k6aSCh2ZKGuAWO+Atk71NI38bxsOXPqyzms6KMEIAaUaN8kOegImb7C12\niZqDdDwz+xdJLznn/sHzeOYgAeM4+IeDWvnISv3whR96n3PxWRfr3uX38kkjgMhRo/yQJ2BiqmIO\nkpnNL/i+SdKfS/pVfBEB1WHGO2fotJmnFW0oWErttFq1zGzhH1QAZUGN8kOegPJIVIMkqcvMnjez\nfkn/S9Jq59zjcQcFVLoRN6KHtj9UtKFgKUdHjuoH238QdOUkACiFGuWHPAHlkagGyTn3WefcfOfc\nAufcuc652+OOCagGT+x5QvsO7pvQOfsO7tPWPVsjiggA3kKN8kOegPJIVIMEIBrrtq8ruiUjv6rR\n6a+dPu7qSLXTarVu+7pyhgkgpahRfsgTUB6JXqRhovKLNOzZs4dFGoCcETeied+Zp98f/P2xx8xM\nc981V9+/+Pv6+ue+rm/88zd02Q8v06v/9qoKa8K7Z7xbL33+JU0zPksBEA1qlB/yBEzc8PCwWltb\npWpaxW6izKxF7IUEAAAA4C0Z59xe34OrrUEySadJOhDokjM12nBlAl4TycRrnS683unC650evNbp\nwuudLpN9vWdK+q2bQNPjt05khcj9wb27wxMZ7bckSQcmMiyHysNrnS683unC650evNbpwuudLlN4\nvSf8/wY3owIAAABADg0SAAAAAOTQIL29I5K+kfsvqhuvdbrweqcLr3d68FqnC693upTt9a6qRRoA\nAAAAYCoYQQIAAACAHBokAAAAAMihQQIAAACAHBokD2b2OTN71sz6zew5M7sh7pgQDTO7IfcaP2tm\nvzKzK+KOCdExsyVm9rSZHTGz1XHHg/DMbJ6ZPWFmL5rZU2b23rhjQjTM7J/MbJeZOTNbEHc8iJaZ\n1ZnZI7m/2780s8fN7Iy440I0zOyx3PuyfjPrM7PzIn0+Fmk4MTNrdM4N5b5vkPScpGXOuWfijQyh\nmdmHJP0/59yQmbVKekbSBc65V2IODREws/8kqV7SxyWd7JxbFXNICMzM/reke51z95hZp6QvOef+\nLO64EJ6ZfVDSq5J+Juki51x/zCEhQmZWJ+nPJf1P55wzs+sldTrnFscbGaJgZn/inPv33PfLJd3i\nnDs3qudjBMlDvjnKmSHpHXHFgmg5536af72dc3sk/U5Sa7xRISrOuRedc7+UdDTuWBCemc2S9H5J\n9+ce+qGkVj5lrk7Ouf/jnBuIOw6Uh3PusHPux+6tT/r/r6Q5MYaECOWbo5xGSZGO8NAgeTKzTjPb\nLmmXpL9n9Kj6mdmHJb1L0lNxxwJgUlolDTrnjkpS7o3UbkmzY40KQBS6JK2POwhEx8zuNbM9kr4p\n6coon6s2yotXCjPbKmleiV+f55zb45zrkdRjZnMkPWxmjzrndpQrRoTh81rnjjtH0t2SLnXOHSxX\nfAjL9/UGAFQuM/sbSWdI+lDcsSA6zrmrJMnMVkr6lqS/iOq5aJAkOecWTuDYXWb2C0kfk0SDVGF8\nXmszmy/pUUnXOOd+Fn1UiMpE/m6jKu2R1Gxmtc65o2ZmGh092h1zXAACMbO/ltQh6cPOuTfjjgfR\nc86tNbN/MbNTnHOvR/Ec3GLnIfeGOf99k0YnBf4qvogQFTM7S9KPJX3GOfd43PEAmDzn3D5J2yTl\nV6O8WNKAc+7l+KICEIqZ3SRphaQLj5ujgipiZn9iZqcV/HyRpNcl/Wtkz8kqdidmZndIapP0B0km\n6Q7n3O3xRoUomNnjGp3U/ZuCh7/knPtJTCEhQrlVC9dKatDo3+0hSdc55zbEGhiCMbMzJd0j6RRJ\nw5I+6Zx7NtagEIncv9VLJL1Ho2+eDjjnWJCjSplZRqOjxK9KOpB7+Ihz7oL4okIUzOw/SvofGl11\ndkTSfkl/HeVKlTRIAAAAAJDDLXYAAAAAkEODBAAAAAA5NEgAAAAAkEODBAAAAAA5NEgAAAAAkEOD\nBAAAAAA5NEgAgNiZ2S4z22Fm/bmvO6dwrVVm9p6Q8QEA0oN9kAAAsTOzXZIuCrHx31SuZWY1zrns\nVGMAAFQuRpAAAIlkZjPN7Htm9qSZ/crM/ruZvTP3u5vM7KncaNNTZrYw9/h/kXSapIdyv1tgZreY\n2eqC615vZvfkvr/azDaZ2Q/N7FlJ55vZe8xsXe55nzWz/1b+Pz0AIC40SACApMg3Nf1mtlzSP0jq\nc86dL+lcjf6b1ZU79j7n3J855xZI+rykuyXJOfdfJf1W0qXOuQWeo0gXSPob59w5zrmtktZK+ufc\n854n6f1m9vGQf1AAQHLVxh0AAAA5lxY2NGZ2h6SFZnZT7qF6Sfnb384zs69IOkXSUUlnmlm9c+7Q\nJJ73CefcjtxzzpD0IUmnmln+9ydLOnMS1wUAVCAaJABAUpmki51zL455cPQ2u15J7c65p8ysQdKQ\npJMkjdcgHZVUU/Bz3XG/f+O455Sk/+ycOzyV4AEAlYlb7AAASfWIpC+ZWa0kmdm7zOwMjTY475S0\nO3fc5487b1hSY8HPL2v0NrkaM5su6eJST+ice0PSJkk35x8zs9PMLDPVPwwAoDLQIAEAkupGjY4I\n9ZvZryT9VNIc59ywpK9KetLMnpb0h+PO+ydJ38sv0qDR0abfSnpB0qOSnjnB814u6Qwzey63cEOv\nRm/lAwCkAMt8AwAAAEAOI0gAAAAAkEODBAAAAAA5NEgAAAAAkEODBAAAAAA5NEgAAAAAkEODBAAA\nAAA5NEgAAAAAkEODBAAAAAA5NEgAAAAAkEODBAAAAAA5NEgAAAAAkPP/AZH2FwHvUw8oAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e589e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_knn_regression(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To do regression, simply use `KNeighborsRegressor` instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "X, y = mglearn.datasets.make_wave(n_samples=40)\n",
    "\n",
    "# split the wave dataset into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "\n",
    "# Instantiate the model, set the number of neighbors to consider to 3:\n",
    "reg = KNeighborsRegressor(n_neighbors=3)\n",
    "# Fit the model using the training data and training targets:\n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The default scoring function for regression models is $R^{2}$. It measures how much of the data variability is explained by the model. Between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      "[-0.054  0.357  1.137 -1.894 -1.139 -1.631  0.357  0.912 -0.447 -1.139]\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set predictions:\\n{}\".format(reg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set R^2: 0.83\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set R^2: {:.2f}\".format(reg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Analysis\n",
    "We can again output the predictions for each possible input, for different values of _k_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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a5ypLGbcLnsO1ieeWfcQPNzSoB3WNS8IN3dVoWQrNcHX/O71H2Xc1PBebVOrU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\nb3QgMSAwIFIgPj4Kc3RhcnR4cmVmCjE3NjA0CiUlRU9GCg==\n",
      "image/png": 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JJk6cGPL5yZMn8/nnn9OnTx/AGeT+tttuA+CJJ55gwoQJdOvWjdNOO41hw4b5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c20637860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# create 1000 data points, evenly spaced between -3 and 3\n",
    "line = np.linspace(-3, 3, 1000).reshape(-1, 1)\n",
    "for n_neighbors, ax in zip([1, 3, 9], axes):\n",
    "    # make predictions using 1, 3 or 9 neighbors\n",
    "    reg = KNeighborsRegressor(n_neighbors=n_neighbors)\n",
    "    reg.fit(X_train, y_train)\n",
    "    ax.plot(line, reg.predict(line))\n",
    "    ax.plot(X_train, y_train, '^', c=mglearn.cm2(0), markersize=8)\n",
    "    ax.plot(X_test, y_test, 'v', c=mglearn.cm2(1), markersize=8)\n",
    "\n",
    "    ax.set_title(\n",
    "        \"{} neighbor(s)\\n train score: {:.2f} test score: {:.2f}\".format(\n",
    "            n_neighbors, reg.score(X_train, y_train),\n",
    "            reg.score(X_test, y_test)))\n",
    "    ax.set_xlabel(\"Feature\")\n",
    "    ax.set_ylabel(\"Target\")\n",
    "_ = axes[0].legend([\"Model predictions\", \"Training data/target\",\n",
    "                \"Test data/target\"], loc=\"best\")"
   ]
  },
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   "cell_type": "markdown",
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    "We see that again, a small _k_ leads to an overly complex (overfitting) model, while a larger _k_ yields a smoother fit."
   ]
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    "## kNN: Strengths, weaknesses and parameters\n",
    "* There are two important hyperparameters:\n",
    "    * n_neighbors: the number of neighbors used\n",
    "    * metric: the distance measure used\n",
    "        * Default is Minkowski (generalized Euclidean) distance.\n",
    "* Easy to understand, works well in many settings\n",
    "* Training is very fast, predicting is slow for large datasets\n",
    "* Bad at high-dimensional and sparse data (curse of dimensionality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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    "Conclusions:\n",
    "\n",
    "- We've covered the main machine learning concepts\n",
    "- We used scikit-learn to build a first model\n",
    "- We met our first algorithm (kNN)\n",
    "\n",
    "- Next lectures: \n",
    "    - Python tutorials\n",
    "    - Model selection\n",
    "    - Increasingly sophisticated algorithms"
   ]
  }
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   "author": "Joaquin Vanschoren",
   "title": "Introduction"
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