Decision Tree Classifier
Implementing Machine Learning Algorithm : Decision Tree Classifier on the dataset of Social Network Ads
Decision Tree Classification: This Classification is based on the decision tree structure. A decision tree is a form of a tree or hierarchical structure that breaks down a dataset into smaller and smaller subsets. At the same time, an associated decision tree is incrementally developed. The tree contains decision nodes and leaf nodes. The decision nodes are those nodes represent the value of the input variable(x). It has two or more than two branches. The leaf nodes contain the decision or the output variable(y). The decision node that corresponds to the best predictor becomes the topmost node and called the root node
How does the Algorithm Work?
This algorithm works based on maximizing the information gain in the groups of data points. That means it splits the data points into optimal parts(subtree) in such a way that it contains as much as information and less randomness. It selects the best attributes using the Attribute Selection Measures to split the data. For the above data points, it would split them in the following way