Decision Tree

What is Decision Tree

A Decision Tree is a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. They are also a popular tool in Machine Learning.

Decision Trees are a very intuitive approach. You are just asking a series of questions about your target, then choose a branch based on your choice. When you split the tree, each node has two branches to split.

Unlike other models, the decision tree is the one with very good interpretability, because you know why you chose the left branch, not the right branch. Therefore, decision trees are widely used in the fields of finance, insurance, and medicine, because these fields want to know the reason for a certain decision when deciding.

Compare with other models

• Simple to understand and to interpret.
• Can handle the category type data.
• Can handle the missing value.
• The reasoning is fast.
• Easy to be overfitting.
• Performance is not good to compare with some models.
• Some tasks can’t be learned.