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What is node impurity in decision tree?
The node impurity is a measure of the homogeneity of the labels at the node. The current implementation provides two impurity measures for classification (Gini impurity and entropy) and one impurity measure for regression (variance).
Why is node purity important?
The decision to split at each node is made according to the metric called purity . A node is 100% impure when a node is split evenly 50/50 and 100% pure when all of its data belongs to a single class. In order to optimize our model we need to reach maximum purity and avoid impurity.
What is node in a decision tree?
A decision tree typically starts with a single node, which branches into possible outcomes. A chance node, represented by a circle, shows the probabilities of certain results. A decision node, represented by a square, shows a decision to be made, and an end node shows the final outcome of a decision path.
Which is the best measure of node impurity?
Gini index is one of the popular measures of impurity, along with entropy, variance, MSE and RSS. I think that wikipedia’s explanation about Gini index, as well as the answers to this Quora question should answer your last question (about Gini index).
How to make a decision in a decision tree?
In the following example, we’ve to approve a loan on the basis of the age, salary, and no. of children the person has. We ask a conditional question at each node and make splits accordingly, till we reach a decision at the leaf node (i.e get loan/don’t get loan).
Which is the parent node in a decision tree?
Parent and Child Node: A node, which is divided into sub-nodes is called the parent node of sub-nodes whereas sub-nodes are the child of the parent node. Decision Node: When a sub-node splits into further sub-nodes, then it is called a decision node.
When to return a pure single node tree?
If S is pure enough, return a single-node tree, labeled with the most common class in S (or with the average of target values in S, in case of a regression tree).