"Understanding the Basics of Decision Trees"


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Decision Trees are a fundamental type of machine learning algorithm which is predominantly used in classification problems but it can be suitably used in regression ones as well. They are simple, yet powerful, and are used for both predictive and descriptive modeling. The simplistic nature of decision trees makes them easy to understand, interpret, and visualize. Despite the simplicity, decision trees are a foundation for some of the most powerful machine learning models including Random Forest and Gradient Boosting.

What is a Decision Tree?

A Decision Tree is a flowchart-like structure, where each internal node denotes a test on an attribute, each branch represents an outcome of that test, and each leaf node (terminal node) holds a class label. The topmost node in a tree is the Root Node. A decision tree is a simple representation for classifying examples. For example, decision trees are useful for tasks such as medical diagnosis, credit scoring, and natural language parsing.

Why use Decision Trees?

Decision trees possess several characteristics that make them attractive for data mining:

  • They are simple to understand and interpret.
  • They can handle both numerical and categorical data.
  • They require little data preparation, such as normalization or scaling.
  • They produce a model that can be visualized and easily explained.

Types of Decision Trees

There are two main types of decision trees: Classification trees and Regression trees. Classification trees are used when the response variable is categorical, i.e., when it is divided into classes. In contrast, regression trees are used when the response variable is numeric or continuous.

Building a Decision Tree

The process of building a decision tree involves a set of methods and techniques:

  • Attribute selection: This step involves selecting the attributes that will provide the best, most informative split in the data.
  • Tree Pruning: It’s a technique of removing sections of the tree that provide little power to classify instances, thereby making the model more efficient.


Decision Trees, despite constituting a basic algorithm, still remain a part of the essential toolkit of any machine learning practitioner. They are the building blocks for a few complex algorithms. Easy to understand, implement, visualize, and interpret, Decision trees have a lot to offer. The concept and philosophy behind Decision Trees yield some very powerful Machine learning models like XGBoost and Random Forests.


  • Q: How is a decision tree built?

    A: A decision tree is built top-down, from a root node and involves partitioning the data into subsets that contain instances with similar values.

  • Q: Can a decision tree handle missing values?

    A: Yes, decision tree algorithms have different ways to handle missing values and one popular way is to use surrogation, which substitutes a variable that most closely resembles the split of the missing variable.

  • Q: What is tree pruning in decision tree?

    A: Tree pruning is done to remove unnecessary branches from the tree. It reduces the complexity of the final classifier, increase the predictive accuracy by reduction of overfitting.

  • Q: What is overfitting in context of decision tree?

    A: In the context of decision trees, overfitting occurs when the tree is designed to perfectly fit all samples in the training data set. Thus, it ends up capturing noise and inaccurately classifying new instances.

  • Q: What is information gain in decision tree?

    A: Information gain is a statistical property that measures how well a given attribute separates the training examples according to their target classification. It is used to decide the splitting attribute in a decision tree.


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