Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.
Overview
Syllabus
- Lesson 1: Bagging in Machine Learning
- Adjust the Number of Estimators
- Train and Evaluate Bagging Classifier
- Optimize Bagging Classifier for Wine Classification
- Enhance Your Bagging Classifier
- Lesson 2: Random Forest in Machine Learning
- Adjusting Random Forest Tree Depth
- Complete the Random Forest Classifier for Wine Dataset
- Improving Random Forest for Wine Classification
- Evaluate Random Forest Accuracy with Varying Depths
- Lesson 3: Boosting with AdaBoost in Machine Learning
- Change the Weak Classifier in AdaBoost
- Train and Predict with AdaBoost
- AdaBoost vs RandomForest
- Lesson 4: Gradient Boosting in Machine Learning
- Adjust Gradient Boosting Estimators
- Complete the Gradient Boosting Setup for Digit Classification
- Gradient Boosting vs. AdaBoost on Synthetic Data
- Comparing Models Efficiency
- Gradient Boosting with Varying Estimators
- Lesson 5: Stacking in Machine Learning
- Change Meta-Model to Gradient Boosting
- Change the Meta-Model in Stacking Classifier
- Complete the Stacking Classifier
- Tune the Stacking Classifier
