'Feature relevance' diaporamas de présentation

Lecture 5: Causality and Feature Selection

Lecture 5: Causality and Feature Selection. Isabelle Guyon isabelle@clopinet.com. Variable/feature selection. Y. X. Remove features X i to improve (or least degrade) prediction of Y. What can go wrong?. Guyon-Aliferis-Elisseeff, 2007. What can go wrong?. X 2. Y. Y. X 2. X 1. X 1.

412 views • 41 slides

Lecture 5: Causality and Feature Selection

Lecture 5: Causality and Feature Selection. Isabelle Guyon isabelle@clopinet.com. Variable/feature selection. Y. X. Remove features X i to improve (or least degrade) prediction of Y. What can go wrong?. Guyon-Aliferis-Elisseeff, 2007. What can go wrong?. X 2. Y. Y. X 2. X 1. X 1.

490 views • 32 slides

Lecture 3: Causality and Feature Selection

Lecture 3: Causality and Feature Selection. Isabelle Guyon isabelle@clopinet.com. Variable/feature selection. Y. X. Remove features X i to improve (or least degrade) prediction of Y. What can go wrong?. Guyon-Aliferis-Elisseeff, 2007. What can go wrong?. X 2. Y. Y. X 2. X 1. X 1.

332 views • 32 slides

Identifying Feature Relevance Using a Random Forest

Identifying Feature Relevance Using a Random Forest. Jeremy Rogers & Steve Gunn. Overview. What is a Random Forest? Why do Relevance Identification? Estimating Feature Importance with a Random Forest Node Complexity Compensation Employing Feature Relevance Extension to Feature Selection.

463 views • 25 slides

Predictive and Contextual Feature Separation for Bayesian Metanetworks

317 views • 20 slides