6. Ensemble & Boosting Methods
Ensemble methods, including Bagging, Boosting, and Stacking, enhance predictive accuracy by combining multiple model predictions. Boosting techniques such as AdaBoost, Gradient Boosting, and XGBoost highlight sequential learning that corrects previous errors, while newer methods like LightGBM and CatBoost improve efficiency and adaptability. These approaches are pivotal in machine learning applications, particularly in scenarios requiring high accuracy.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Ensemble methods combine multiple models to improve prediction robustness.
- Bagging reduces variance by training on bootstrapped samples.
- Boosting focuses on correcting errors from previous models, thus reducing bias.
Key Concepts
- -- Ensemble Methods
- Techniques that combine predictions from multiple models to achieve better performance.
- -- Bagging
- A method that builds multiple models from random subsets of the training data and combines their predictions.
- -- Boosting
- A sequential ensemble method that builds models iteratively, each model focusing on correcting the errors of the previous one.
- -- AdaBoost
- An adaptive boosting algorithm that adjusts the weights of incorrectly classified instances to improve the learning of subsequent models.
- -- Gradient Boosting
- An approach that combines weak learners in a sequential manner to minimize the residuals of the prediction errors.
- -- XGBoost
- An optimized version of gradient boosting that incorporates regularization and parallel computation for enhanced performance.
- -- Stacking
- Integrating the predictions of several base models together using a meta-model to improve overall accuracy.
Additional Learning Materials
Supplementary resources to enhance your learning experience.