CART Method(Classification And Regression Trees) in Decision Tree
We have covered Decision tree and ID3 algorithm in my last blogs.
In this blog we will cover another decision tree algorithm known as CART which stands for Classification And Regression Trees. This blog will give a small overview on CART method.
Decision Tree: — Set of rules used to classify data into categories. Basically, a greedy divide and conquer algorithm.
CART — Classification and Regression Tree — A hypernym that describes decision tree algorithms used for classification and regression (supervised) learning tasks. This technique aimed to create rules that predict the value of target variable from known values of Independent variables.
Term CART was introduced by Leo Breiman in order to refer Decision Tree algorithms, that can be used for classification or regression problems.
CART is a binary tree build by splitting node into two child nodes repeatedly. Each root node represents a single input variable (x) and a split point on that variable. Dataset is split into number of trees depending on the criteria of splitting. These criteria are:- Gini, Entropy and Variance. The splitting is done till the terminal node of the tree is reached.
Decision tree algorithms in their simplest form are hierarchical if-else statements that can be applied to predict a result based upon data. The if-else statements are chosen to maximize a notion of information gain — it reduces the variability in the underlying two children nodes.
If Height > 180 cm Then Male
If Height <= 180 cm AND Weight > 80 kg Then Male
If Height <= 180 cm AND Weight <= 80 kg Then Female
Below is the screenshot of this rule.
Thank you for reading this blog.
ID3 Algorithm in DT : — https://medium.com/@sid-sharma1990/id3-iterative-dichotomiser-3-in-decision-tree-e4f4e2eb9944
Decision Tree: — https://medium.com/@sid-sharma1990/decision-tree-and-its-types-76db66644622
