Machine Learning Algorithms: Random Forest

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Date Added: 7th November 2018

Date Removed: 22nd April 2019

Available for: 6 months

 

Description:

Provides steps for applying random forest to do classification and prediction. Includes why and when it is used, benefits, number of trees, number of variables tried at each step, data partitioning, prediction and confusion matrix, accuracy and sensitivity, randomForest & caret packages, bootstrap samples and out of bag error, and variable importance. Detailed example with cardiotocographic data.

 

Certificate: Not Rated

Year: 2017

Duration: 30 min

 

Director: Bharatendra Rai

Cast: Bharatendra Rai

Studio: Bharatendra Rai

 

Amazon Rating: