Decision Trees in Machine Learning: Understanding the Basics

Decision Trees are a widely used Supervised Learning algorithm that is used for both classification and regression tasks.

They are simple to understand and interpret, and provide a clear visualization of the decision-making process.

In this article, we will discuss the basics of decision trees, how they work, and their applications in various domains.

How Decision Trees Work

Image from “Masters In Data Science”

A decision tree is a flowchart-like tree structure, where each internal node represents a feature(or attribute), each branch represents a decision rule, and each leaf node represents the outcome.

The topmost node in a decision tree is known as the root node. It is the entry point to the tree and represents the entire population or sample.

The tree is constructed by recursively splitting the dataset into subsets based on the values of the feature that results in the most homogeneous sets (or most information gain) in terms of the target variable.

The process stops when the tree reaches a certain depth or when the leaf nodes contain a minimum number of observations.

Classification and Regression

Decision trees can be used for both classification and regression tasks. In classification, the target variable is categorical, and the tree is used to predict the class of an observation.

In regression, the target variable is continuous, and the tree is used to predict the value of the target variable.

Advantages and Disadvantages

Decision Trees have several advantages. They are simple to understand and interpret, and provide a clear visualization of the decision-making process.

They are also relatively robust to outliers and missing values.

However, they are prone to overfitting and may not be able to capture complex relationships in the data.

Applications

Decision Trees are used in a wide range of applications, including financial analysis, medical diagnosis, and customer segmentation.

They are also used in feature selection and feature engineering, which are important steps in building predictive models.

Conclusion

In conclusion, Decision Trees are a powerful and widely used Supervised Learning (a Machine Learning subfield) algorithm that is used for both classification and regression tasks.

They are simple to understand and interpret, and provide a clear visualization of the decision-making process.

However, they are prone to overfitting and may not be able to capture complex relationships in the data.

Despite their limitations, they are used in a wide range of applications, including financial analysis, medical diagnosis, and customer segmentation.

Similar Posts