Advanced Supervised Learning & Evaluation (Weeks 7)
Ensemble methods in supervised learning combine multiple models to enhance prediction accuracy, mitigate overfitting, and improve resilience against noisy data. They primarily consist of two approaches: Bagging, focusing on averaging models to reduce variance, and Boosting, which sequentially trains models to correct errors from previous ones. The chapter explores various algorithms under these methods, such as Random Forest for Bagging and AdaBoost alongside Gradient Boosting Machines for Boosting, highlighting their functionalities and advantages in practical applications.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Ensemble learning combines multiple models to improve predictive accuracy.
- Bagging reduces variance by training models independently on random subsets of data, while Boosting reduces bias by sequentially correcting errors.
- Random Forest is a popular Bagging algorithm, and modern Boosting methods like XGBoost, LightGBM, and CatBoost enhance performance and scalability.
Key Concepts
- -- Ensemble Learning
- A machine learning paradigm where multiple models are trained to solve the same problem and their predictions are combined to achieve better performance.
- -- Bagging
- A technique that reduces variance by training multiple copies of a model independently on bootstrapped samples of the training dataset.
- -- Boosting
- A method that reduces bias by training models sequentially, where each new model focuses on correcting the errors of its predecessors.
- -- Random Forest
- An ensemble method that uses Bagging with decision trees to enhance prediction accuracy and generalization by averaging results from many independent trees.
- -- XGBoost
- An optimized version of gradient boosting which offers high performance and speed through advanced regularization techniques and parallelization.
Additional Learning Materials
Supplementary resources to enhance your learning experience.