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Decision Tree Hyperparameters : max_depth, min_samples_split, min_samples_leaf, max_features

In this video we will explore the most important hyper-parameters of Decision tree model and how they impact our model in term of over-fitting and under-fitting. The important hyper-parameters of a decision tree are max_depth, min_samples_split, min_samples_leaf, max_features, criterion. The difference between min_samples_split & min_samples_leaf is taken from an amazing answer provided on stackoverflow, link : https://stackoverflow.com/questions/4... If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: Blog - http://bhattbhavesh91.github.io Twitter -   / _bhaveshbhatt   GitHub - https://github.com/bhattbhavesh91 Medium -   / bhattbhavesh91   #DecisionTree #Hyperparameters #maxdepthdecisiontree