1/7/2024 0 Comments Visualize decision tree pythonDecision Tree Classification in Scikit-Learn While decision trees can estimate the homogeneity of leaves using the entropy / information gain or Gini impurity on categorical variables they calculate the reduction in variance to estimate the purity of leaves on continuous variables. Variance reduction, or mean square error, is a technique used to estimate the purity of the leaves in a decision tree when dealing with continuous variables. Gini is the sum of squares of probabilities for each class.Īnd, then, again, the model will estimate the purity of the split by computing the weighted Gini impurity of both children leaves compared to the Gini impurity of the parent. Computing the probability of incorrecty classifying a randomly chosen element.Selecting elements at random and atributing the same class.The purer, or homogenous, a node is, the smaller the Gini impurity is. Gini impurity is used as an alternative to information gain (IG) to compute the homogeneity of a leaf in a less computationally intensive way. The decision tree tries to maximize the information gain. If the weighted entropy is smaller than the entropy of the parent node, then the information gain is greater.Ībove, the entropy of the parent equals 1 and the weighted entropy equals 1.09.Ī split like the one above would reduce the entropy. Calcute the weighted average entropy of the split.Calculate the entropy of the children nodes.Calculate the entropy of the parent node.The smaller the entropy, the higher the homogeneity of the nodes. Where pi represents the percentage of the class in the node.īelow is a representation of the calculation of the entropy on a dataset where there are 2 values in the class split in 30%-70%. The formula of entropy in decision trees is: To measure the information gain of a parent against its children, we must subtract the weighted entropy of the children from the entropy of the parent.Įntropy is used to measure the quality of a split for categorical targets. The information gain helps define if the split contains more pure nodes compared to the parent node. Information Gain of a single node is calculated by subtracting the entropy to 1. To produce the “best” result, decision trees aim at maximizing the Information Gain (IG) after each split. Identify with feature and which split point: Information Gain (IG), Reduction in varianceįor decision tree classification problems on categorical variables:įor decision tree regression problems on continuous variables:.Measure the quality of a split: gini or entropy.Decision Tree Metricsĭecision trees try to produce the purest leaves in a recursive way by splitting nodes into smaller sub-nodes.īut how does it choose how to split the nodes, evaluate the purity of a leaf, or decide when to stop? They are called ensemble learning algorithms. Some techniques use more than one decision tree. Regression decision tree (used for continuous data).Classification decision tree (used for categorical data).There are 2 decision trees grouped under Classification and decision tree (CART). MARS (Multivariate Adaptive Regression Splines).Chi-square (Chi-square automatic interaction detection).CART (Classification And Regression Tree).There are multiple decision tree algorithms: Maximum depth: maximum number of branches between the top and the lower end.The sub-nodes are the child nodes of the parent from which they divided. Parent / child nodes: A node that is divided in sub-nodes is called a parent node.Branches: A subsection of the entire tree (also known as a sub-tree).They are the nodes that produce the prediction. Leaf nodes: Nodes that have 1 parent, but do not split further (also known as terminal nodes).Decision nodes: Nodes that have 1 parent node and split into children nodes (decision or leaf nodes).It has no parent node and 2 children nodes Root node: First node in the path from which all decisions initially started from.Susceptible to overfitting when unconstrainedĭecision trees are simple models that have branches, nodes and leaves and break down a dataset into smaller subsets containing instances with similar values.Sensitive to small variations in the training data.On the other hand, trees do have some disadvantages: Simple to use as no data preprocessing needed.Flexible as they can describe non-linear data. Decision tree algorithms have multiple advantages:
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