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Out Of Bag(OOB) Evaluation And Error In Random Forest Indepth Intuition In Hindi

Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). Bagging uses subsampling with replacement to create training samples for the model to learn from. OOB error is the mean prediction error on each training sample xi, using only the trees that did not have xi in their bootstrap sample -------------------------------------------------------------------------------------------------------------------- Support my channel by taking up membersship, this will help to upload more free videos series    / @krishnaikhindi   ----------------------------------------------------------------------------------------------------------------------- Subcribe @Krish Naik for Data Science Videos In Hindi --------------------------------------------------------------------------------------------------------------------- All Playlist links are given below ML playlist in hindi: https://bit.ly/3NaEjJX Stats Playlist In Hindi:https://bit.ly/3tw6k7d Python Playlist In Hindi:https://bit.ly/3azScTI ------------------------------------------------------------------------------------------------------------------- Connect with me here: Twitter:   / krishnaik06   Facebook:   / krishnaik06   instagram:   / krishnaik06