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 : stackoverflow.com/questions/4...
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