Breiman Random Forests Overview
"Explore the principles and techniques behind Breiman Random Forests, including bootstrapping, bagging, and the random subspace method. Learn how BRFs offer robust classification and regression while avoiding overfitting, making them faster than other methods like Adaboost. Dive into decision trees, random subspaces, and the strengths of BRFs in handling outliers and noise."
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Presentation Transcript
BREIMAN RANDOM FORESTS MEGHANA KILLI, NAM TRAN
Introduction Machine Learning Deterministic ~ Random Strong Law of Large Numbers Eg: AdaBoost Eg: Random Forests 1 2 3 2
Decision Trees 3 Image source: Elements of Statistical Learning by Hastie et.al.
Random Forests Bootstrapping - Random Sampling Technique with Replacement Random Forests Bagging (B)ootstrap (Agg)regat(ing) {1 2 3 4 5 6} {1 1 2 2 3 3}, {1 1 1 2 2 2}, {1 2 3 4 5 5}, {5 5 6 6 6 6} Decision Trees 4
Random Subspace Method Total number of features = N Generate a subspace at random with m (<N) features Select features from subspace to best split data 5
Decision Trees in a Subspace 6 Image source: Elements of Statistical Learning by Hastie et.al.
Breiman Random Forests Bootstrapping - Random Sampling Technique with Replacement Breiman Random Forests Random Forests Random Subspace Method Bagging (B)ootstrap (Agg)regat(ing) Decision Trees 7
Features of BRFs Classification and Regression Avoid overfitting Insensitive to number of features sampled Robust to outliers and noise - more than Adaboost Faster than bagging and boosting 8
