[Concept Entry] Sklearn: Stochastic Gradient Descent (#5822)

* [Concept Entry] Sklearn: Stochastic Gradient Descent

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+---
+Title: 'Stochastic Gradient Descent'
+Description: 'Stochastic Gradient Descent (SGD) aims to find the best set of parameters for a model that minimizes a given loss function.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'Machine Learning'
+  - 'Scikit-learn'
+  - 'Supervised Learning'
+  - 'Unsupervised Learning'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In [Sklearn](https://www.codecademy.com/resources/docs/sklearn), **Stochastic Gradient Descent (SGD)** is a popular optimization algorithm that focuses on finding the best set of parameters for a model that minimizes a given loss function.
+
+Unlike traditional [gradient descent](https://www.codecademy.com/resources/docs/ai/search-algorithms/gradient-descent), which calculates the gradient using the entire dataset, SGD computes the gradient using a single training example at a time. This makes it computationally efficient for large datasets.
+
+Sklearn provides two primary classes for implementing SGD:
+
+- `SGDClassifier`: Well-suited for classification tasks. Supports various loss functions and penalties for fitting linear classification models.
+- `SGDRegressor`: Well-suited for regression tasks. Supports various loss functions and penalties for fitting [linear regression models](https://www.codecademy.com/resources/docs/sklearn/linear-regression-analysis).
+
+## Syntax
+
+Following is the syntax for implementing SGD using `SGDClassifier`:
+
+```pseudo
+from sklearn.linear_model import SGDClassifier
+
+# Create an SGDClassifier model
+model = SGDClassifier(loss="hinge", penalty="l2", max_iter=1000, random_state=42)
+
+# Fit the classifier to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+```
+
+Following is the syntax for implementing SGD using `SGDRegressor`:
+
+```pseudo
+from sklearn.linear_model import SGDRegressor
+
+# Create an SGDRegressor model
+model = SGDRegressor(loss="squared_loss", penalty="l2", max_iter=1000, random_state=42)
+
+# Fit the regressor to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+```
+
+- `loss`: Specifies the loss function.
+  - For `SGDClassifier`, the options include `hinge` (default), `log`, and `modified_huber`.
+  - For `SGDRegressor`, the options include `squared_loss` (default), `huber`, and `epsilon_insensitive`.
+- `penalty`: Specifies the regularization penalty. Common options include `l2` (L2 regularization, default), `l1` (L1 regularization), and `elasticnet` (a combination of L1 and L2 regularization).
+- `max_iter`: Specifies the maximum number of iterations for the optimization algorithm. The default value is `1000`. Excessive values can lead to overfitting or unnecessary computations.
+- `random_state`: Specifies the random seed for reproducibility. The default value is `None`. Setting `random_state` ensures consistent results across runs by fixing the randomness of data splitting or model initialization.
+
+## Example
+
+The following example demonstrates the implementation of SGD using `SGDClassifier`:
+
+```py
+from sklearn.datasets import load_iris
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data
+y = iris.target
+
+# Create training and testing sets by splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Create an SGDClassifier model
+model = SGDClassifier(loss="hinge", penalty="l2", max_iter=1000, random_state=42)
+
+# Fit the classifier to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+
+# Evaluate the model's accuracy
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)
+```
+
+The above code produces the following output:
+
+```shell
+Accuracy: 0.8
+```
+
+## Codebyte Example
+
+The following codebyte example demonstrates the implementation of SGD using `SGDRegressor`:
+
+```codebyte/python
+from sklearn.datasets import load_diabetes
+from sklearn.linear_model import SGDRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# Load the Diabetes dataset
+diabetes = load_diabetes()
+X = diabetes.data
+y = diabetes.target
+
+# Create training and testing sets by splitting the dataset
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Create an SGDRegressor model
+model = SGDRegressor(loss="squared_loss", penalty="l2", max_iter=1000, random_state=42)
+
+# Fit the regressor to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the new data
+y_pred = model.predict(X_test)
+
+# Evaluate the model's performance
+m2e = mean_squared_error(y_test, y_pred)
+
+print("Mean Squared Error:", m2e)
+```