Understanding the Decision Tree [ Day 1]
Hello Folks, Today we will explore the Decision Tree in Machine Learning. The decision tree is usually called CART (Classification and Regression Trees). 1.DecisionTreeClassifier: class sklearn.tree. DecisionTreeClassifier ( * , criterion = 'gini' , splitter = 'best' , max_depth = None , min_samples_split = 2 , min_samples_leaf = 1 , min_weight_fraction_leaf = 0.0 , max_features = None , random_state = None , max_leaf_nodes = None , min_impurity_decrease = 0.0 , class_weight = None , ccp_alpha = 0.0 ) Advantages : 1.Easy to Understand Disadvantages: Decision trees tend to overfit data with a large number of features. Getting the right ratio of samples to a number of features is important since a tree with few samples in high dimensional space is very likely to overfit. Purning: (limit the height of tree) Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tr
