About decision trees

This is an article about decision trees.

hello!

Today we will learn about decision trees.

Decision Tree is one of the supervised learning algorithms used to classify or predict data. It uses a tree structure to classify or predict data according to several rules.

We will explain the decision tree below.

Main Features

Tree structure

A decision tree has a tree structure consisting of nodes and edges, and each node classifies or branches data according to specific conditions.

Rule-based learning

Each node in the tree classifies or predicts data based on specific rules (conditions).

Ease of interpretation

A decision tree is an intuitive and easy-to-interpret model that makes it easy to visually understand how things are classified based on what conditions.

How it works

Select split criteria

When splitting data, choose the best criteria (conditions) to split it into subgroups with the highest purity possible.

Recursive division

Split the data based on the selected criteria, and continue growing the tree by selecting split criteria again for each subgroup.

Pruning

Prune the tree at an appropriate level to prevent overfitting and improve generalization ability.

uses

Classification problem

Used to predict a categorical target variable. For example, it is used to predict whether a customer will buy a product.

Regression problem

Used to predict a continuous target variable. For example, it is used to predict house prices.

Main Algorithm

Classification and Regression Trees (CART)

It is a decision tree algorithm that can be used for both classification and regression problems.

ID3(Iterative Dichotomizer 3)

It is an algorithm that divides based on the criterion that maximizes information gain.

Conclusion

Decision trees have excellent interpretability and are used for classification and prediction problems in a variety of fields.

thank you!