Added a new entry for Decision Trees (#5775)

* Added a new entry for Decision Trees

* Update decision-trees.md

minor fix

* Update content/sklearn/concepts/decision-trees/decision-trees.md

Co-authored-by: Pragati Verma <[email protected]>

* Update content/sklearn/concepts/decision-trees/decision-trees.md

Co-authored-by: Pragati Verma <[email protected]>

* Update content/sklearn/concepts/decision-trees/decision-trees.md

Co-authored-by: Pragati Verma <[email protected]>

* Update decision-trees.md

added a detailed syntax

* Review Fixes

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content/sklearn/concepts/decision-trees/decision-trees.md

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+---
+Title: 'Decision Trees'
+Description: 'Decision trees are machine learning models that split data into branches based on features, enabling clear decisions for classification and regression tasks.'
+Subjects:
+  - 'Data Science'
+  - 'Machine Learning'
+Tags:
+  - 'Machine Learning'
+  - 'Scikit-learn'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+**Decision Trees** are intuitive machine learning algorithms that can be used for classification and regression tasks. They recursively split data into subsets based on feature values, creating a tree-like structure to make predictions.
+
+Decision trees are widely favored for their ease of use and clarity in interpretation.
+
+In [sklearn](https://www.codecademy.com/resources/docs/sklearn), decision trees are implemented in the `DecisionTreeClassifier` and `DecisionTreeRegressor` classes. They can handle both numerical and categorical data, and support advanced features like pruning and handling missing values.
+
+## Advantages of Decision Trees
+
+- Easy to understand and interpret.
+- Can handle both numerical and categorical data.
+- Requires minimal data preprocessing (e.g., no need for feature scaling).
+- Can model non-linear relationships.
+
+## Limitations
+
+- Can be prone to overfitting without proper pruning.
+- Sensitive to small changes in the data, which can lead to different tree structures.
+
+## Syntax
+
+Below is an example of the syntax for using a decision tree classifier:
+
+```pseudo
+from sklearn.tree import DecisionTreeClassifier
+
+# Initialize the Decision Tree Classifier with common parameters
+model = DecisionTreeClassifier(criterion='gini', max_depth=None, random_state=None, min_samples_split=2, min_samples_leaf=1)
+
+# Fit the model to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the test data
+y_pred = model.predict(X_test)
+```
+
+- `criterion`: Specifies the metric to evaluate split quality (`gini` for Gini Impurity, `entropy` for Information Gain).
+- `max_depth`: Sets the maximum depth of the tree to control model complexity and prevent overfitting.
+- `random_state`: Ensures reproducibility by setting the random seed for consistent results.
+- `min_samples_split`: Minimum number of samples required to split an internal node, controlling when splitting occurs.
+- `min_samples_leaf`: Minimum number of samples required at a leaf node, ensuring leaves aren’t too small.
+
+## Example
+
+Here's an example of a Decision Tree Classifier for predicting labels on a synthetic dataset:
+
+```py
+from sklearn.datasets import make_classification
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Generating a synthetic classification dataset
+X, y = make_classification(n_samples=100, n_features=4, random_state=42)
+
+# Splitting data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Initializing and training the Decision Tree Classifier
+model = DecisionTreeClassifier(max_depth=3, random_state=42)
+model.fit(X_train, y_train)
+
+# Making predictions
+y_pred = model.predict(X_test)
+
+# Evaluating the model
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)
+```
+
+The code above generates an output as follows:
+
+```shell
+Accuracy: 0.9333
+```
+
+> **Note**: The output may change based on dataset randomness, train-test split, hyperparameters, or changes in sklearn and execution environment.