Ensembles in Machine Learning

Learn about ensemble learning techniques, such as bagging, boosting, and stacking, which combine multiple models to achieve superior predictive performance.

Lessons and practices

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

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