In this post, you will learn about how to train a decision tree classifier machine learning model using Python. The following points will be covered in this post:
- What is decision tree?
- Decision tree python code sample
What Is a Decision Tree?
Simply speaking, the decision tree algorithm breaks the data points into decision nodes resulting in a tree structure. The decision nodes represent the question based on which the data is split further into two or more child nodes. The tree is created until the data points at a specific child node is pure (all data belongs to one class). The criteria for creating the most optimal decision questions is the information gain. The diagram below represents a sample decision tree.
Introduction to Classification Algorithms
The idea of Classification Algorithms is pretty simple. You predict the target class by analyzing the training dataset. This is one of the most — if not the most essential — concepts you study when you learn data science.
You might also like: Top Machine Learning Algorithms You Should Know to Become a Data Scientist
What Is Classification?
We use the training dataset to get better boundary conditions that could be used to determine each target class. Once the boundary conditions are determined, the next task is to predict the target class. The whole process is known as classification.