Contents
What are the stopping criteria in decision trees?
The stopping criteria used by CTREE are typical of many decision tree programs. Number of cases in the node is less than some pre-specified limit. Purity of the node is more than some pre-specified limit.
What is the significance of tree pruning in decision tree algorithm?
Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arises in a decision tree algorithm is the optimal size of the final tree.
How is the chi square calculated in a decision tree?
Take the sum of Chi-Square values for all the classes in a node to calculate the Chi-Square for that node. Higher the value, higher will be the differences between parent and child nodes, i.e., higher will be the homogeneity.
Can you understand higher the value of chi square?
Add details and clarify the problem by editing this post . Closed 4 years ago. I can understand higher the value of chi square more significant is the split, but cannot understand the formula used. Please explain intuitively.
How to split a decision tree using information gain?
Steps to split a decision tree using Information Gain: 1 For each split, individually calculate the entropy of each child node 2 Calculate the entropy of each split as the weighted average entropy of child nodes 3 Select the split with the lowest entropy or highest information gain 4 Until you achieve homogeneous nodes, repeat steps 1-3 More
When to use reduction in variance in a decision tree?
Reduction in Variance is a method for splitting the node used when the target variable is continuous, i.e., regression problems. It is so-called because it uses variance as a measure for deciding the feature on which node is split into child nodes. Variance is used for calculating the homogeneity of a node.