Added Gradient Boosting Classifier Explanation

en/Machine Learning/Gradient Boosting Classifier.md

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+# Gradient Boosting Classifier
+
+### Description
+[Gradient Boosting Classifier](https://en.wikipedia.org/wiki/Gradient_boosting#:~:text=Gradient%20boosting%20is%20a%20machine,residuals%20used%20in%20traditional%20boosting.) is an ensemble learning algorithm that combines the predictions of multiple weak learners (usually decision trees) to create a robust and accurate predictive model. It builds a sequence of models, each correcting the errors of the previous one, and combines them to improve overall performance.
+
+### Time Complexity
+The time complexity of training a Gradient Boosting Classifier is influenced by the number of weak learners (n_estimators), the complexity of each weak learner, and the size of the training dataset.
+
+### Space Complexity
+The space complexity is primarily determined by the memory required to store the trained weak learners and the input features.
+
+### Applications
+Gradient Boosting Classifiers are widely used in various applications, including but not limited to:
+- Classification tasks
+- Regression tasks
+- Anomaly detection
+- Ranking problems
+
+### Founder's Name
+The algorithm's development is credited to Jerome Friedman, who introduced gradient boosting in 1999.
+
+### Steps
+1. **Initialization:** Set the number of weak learners (n_estimators) and learning rate.
+2. **Training:** Iterate through the specified number of weak learners.
+   - Calculate pseudo-residuals based on the negative gradient of the logistic loss.
+   - Train a weak learner (Decision Tree with max depth 1) on the pseudo-residuals.
+   - Update the model by adding the weak learner with a scaled learning rate.
+3. **Prediction:** Combine predictions from all weak learners to make the final predictions.
+
+### Maths Intuition
+
+#### Objective:
+Minimize a loss function by iteratively adding weak learners (typically decision trees) to the ensemble.
+
+#### Ensemble Model:
+$ F(x) = \sum_{m=1}^{M} \eta \cdot f_m(x) $
+
+### Loss Function:
+$ L(y, F(x)) = \sum_{i=1}^{N} L(y_i, F(x_i)) $
+
+#### Steps:
+1. **Initialization:** Start with an initial model $ F_0(x) $.
+2. **Iteration:** For $ m = 1 $ to $ M $:
+    - Compute negative gradient ($-\frac{\partial L(y, F_{m-1}(x))}{\partial F_{m-1}(x)}$) as pseudo-residuals.
+    - Train a weak learner ($f_m(x)$) to predict the pseudo-residuals.
+    - Update ensemble: $ F_m(x) = F_{m-1}(x) + \eta \cdot f_m(x) $.
+
+#### Prediction:
+$ F(x) = \sum_{m=1}^{M} \eta \cdot f_m(x) $
+
+#### Regularization:
+- Control learning rate ($\eta$).
+- Limit weak learner complexity (e.g., shallow trees).
+- Introduce shrinkage ($\eta < 1$) to prevent overfitting.
+
+#### Outcome:
+A robust predictive model combining the strengths of multiple weak learners.
+
+### Example
+Consider the following example using the Iris dataset:
+```python
+clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
+iris = load_iris()
+X, y = iris.data, iris.target
+clf.fit(X, y)
+y_pred = clf.predict(X)
+```
+
+### Implementation
+[Link to Gradient Boosting Classifier implementation](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_classifier.py)
+
+### Video URL
+[Link to Video Explanation](https://youtu.be/StWY5QWMXCw?si=IHsnAYjbM6yD-Zdd)
+
+### Wikipedia Page
+[Gradient Boosting Wikipedia](https://en.wikipedia.org/wiki/Gradient_boosting#:~:text=Gradient%20boosting%20is%20a%20machine,residuals%20used%20in%20traditional%20boosting.)
+
+### Summary
+The Gradient Boosting Classifier is an ensemble learning algorithm that builds a sequence of weak learners to create a robust predictive model. It is widely used for classification and regression tasks, and its development is credited to Jerome Friedman. The algorithm's implementation can be found [here](https://github.com/TheAlgorithms/Python/blob/master/machine_learning/gradient_boosting_classifier.py), and additional details can be found in the [video explanation](https://youtu.be/StWY5QWMXCw?si=IHsnAYjbM6yD-Zdd). Contributions to translations or explanations are encouraged, following the outlined guidelines.