Decision Tree Important Hyper parameters

• Optimization Criteria : Gini impurity or Entropy/Info gain
• Max Depth - Build trees with max. d depth deep; d = Number of nodes from top
• min_samples_split: The minimum number of samples required to split an internal node
• min_samples_leaf: The minimum number of samples required to be at a leaf node
• max_features: The number of features to consider when looking for the best split
• min_impurity_decrease: A node will be split if this split induces a decrease of the impurity greater than or equal to this value
• class_weight:Weights associated with classes in the form {class_label: weight}