Dive into the world of decision trees and ensemble methods in this comprehensive 2.5-hour course. Explore fundamental concepts such as impurity functions, CART algorithm, and basic properties of decision trees. Learn about regularization techniques and how to implement decision trees using Scikit-learn. Delve into advanced topics like bias-variance trade-off, ExtraTrees algorithm, bootstrap, bagging, and random forests. Discover the power of ensemble methods, including proximities, feature importance, and various boosting techniques such as gradient boosting, XGBoost, LightGBM, and CatBoost. Gain practical experience through examples and notebook exercises, equipping you with the skills to apply these powerful machine learning techniques to real-world problems.
Overview
Syllabus
DTEL1 - 1 Welcome to DTE class.
DTEL1 - 2 Class Introduction.
DTEL1 - 3 Introduction to Decision Trees.
DTEL1 - 4 Impurity Functions.
DTEL1 - 5 CART Algorithm.
DTEL1 - 6 Basic Properties of Decision Trees.
DTEL1 7 Basic Regularization of Trees.
DTEL1 - 8 Sklearn Trees.
DTEL1 - 9 Conclusion.
DTEL2 2 1 Introduction.
DTEL2 2 2 Bias variance trade off.
DTEL2 2 3 Bias variance Decomposition.
DTEL2 2 4 Generalizations of bias variance tradeoff.
DTEL2 2 5 ExtraTrees Algorithm.
DTEL2 2 6 ExtraTrees with Sklearn.
DTEL2 2 7 Conclusion.
DTEL3 3 1 Introduction.
DTEL3 3 2 Bootstrap.
DTEL3 3 3 Bagging.
DTEL3 3 4 1 Example.
DTEL3 3 5 Random Forest.
DTEL3 3 4 2 Example Notebook.
DTEL3 3 6 General Ensembling.
DTEL4 4 1 Introduction.
DTEL4 4 2 Proximities.
DTEL4 4 3 Proximities Visualizations.
DTEL4 4 4 Feature Importance's.
DTEL4 4 5 Limitations of Tree Feature Importance.
DTEL4 4 6 Feature Importance's in Random Forest.
DTEL4 4 7 Summary.
DTEL5 5 1 Introduction.
DTEL5 5 2 Boosting.
DTEL5 5 3 Gradient Boosting.
DTEL5 5 4 XGBoost.
DTEL5 5 5 LightGBM.
DTEL5 5 6 CatBoost.
Taught by
Machine Learning University
