Goals

• Boosting as a powerful ensemble machine learning technique
• Boosting for regression problems (GradientBoosting Regressor)
• Boosting for classification problems (GradientBoostingClassifier)
• Difference between AdaBoost and Gradient Boosting
• Understand hyperparameters associated to Boosting (learning rate, number of estimators ...)
• GridSearch for optimal hyperparameters for Boosting
• Performance of Boosting compared to other machine learning algorithms

Morning:

• Exercise: individual.md

Afternoon:

• Exercise: pair.md