Gradient Boosting is a technique used for building predictive models Model Building, particularly in tasks like regression and classification. It combines the concepts of Boosting and Gradient Descent to create strong models by sequentially combining multiple Weak Learners (Decision Tree.

Key Idea: Instead of fitting a single strong model, Gradient Boosting builds multiple weak learners sequentially. Each new model focuses on correcting the mistakes made by the previous ones by fitting to the residuals (differences between observed and predicted values).

Gradient Boosting builds an ensemble of Weak Learners (usually Decision Tree) sequentially. Each new model focuses on the errors of the previous ones, aiming to minimize the residual errors.

Final Prediction: The final prediction is made by aggregating the predictions of all the weak models, usually through a weighted sum.

High Performance: Known for its high performance and efficiency in terms of speed and memory usage.

Watch Video Explanation

Key Components

  • Weak Learners: Typically decision trees used in the ensemble.
  • Loss Function: Measures how well the model fits the data.
  • Learning Rate: Controls the contribution of each weak learner to the final model.

Examples

Benefits