Decision Trees and Ensemble Methods
Offered By: YouTube
Course Description
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
Related Courses
Modélisez vos données avec les méthodes ensemblistesCentraleSupélec via OpenClassrooms Predictive Analytics using Machine Learning
University of Edinburgh via edX Ensemble Methods in Python
DataCamp Ensemble Machine Learning in Python: Random Forest, AdaBoost
Udemy Decision Trees, Random Forests, Bagging & XGBoost: R Studio
Udemy