Gradient boosting is a machine learning technique that belongs to the class of ensemble learning methods. It is used for both regression and classification tasks and is known for its ability to build powerful predictive models. Gradient boosting combines the predictions from multiple weak learners (typically decision trees) to create a strong predictive model.
Here's how gradient boosting works:
Weak Learners (Base Models): Gradient boosting starts with a set of weak learners, often simple decision trees with limited depth (stumps). These weak learners are referred to as "base models" or "base learners."
Sequential Training: Gradient boosting trains the base models sequentially. In each iteration, a new base model is trained to correct the errors made by the combined predictions of the previously trained models.
Error Correction: The new base model is trained to minimize the residual errors between the actual target values and the predictions made by the ensemble of previously trained models. It focuses on the examples that the ensemble has difficulty predicting correctly.
Weighted Combination: After training each base model, its predictions are combined with the predictions of the previously trained models. The combination is weighted, giving more importance to better-performing models and less importance to those that make larger errors.
Iterative Process: This process is repeated for a predefined number of iterations or until a specified stopping criterion is met. Each new model is trained to capture the patterns and errors that were not effectively addressed by the previous models.
Final Prediction: The final prediction is obtained by aggregating the predictions of all base models. For regression tasks, this is often done by averaging the predictions. For classification tasks, a voting or weighted voting scheme is used to determine the class label.
Gradient boosting algorithms, such as XGBoost (Extreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and AdaBoost (Adaptive Boosting), differ in the specific techniques they use for error correction, weighting, and regularization. They also offer various hyperparameters to control the learning process.
Gradient boosting is popular in machine learning competitions like Kaggle due to its ability to achieve high predictive accuracy, robustness against overfitting, and flexibility in handling complex data patterns. It often serves as a winning solution for a wide range of data science and predictive modeling tasks.