Decision Tree Algorithm – A Complete Guide
This article was published as a part of the Data Science Blogathon Introduction Till now we have learned about linear regression, logistic regression, and they were pretty hard to understand. Let’s now start with Decision tree’s and I assure you this is probably the easiest algorithm in Machine Learning. There’s not much mathematics involved here. Since it is very easy to use and interpret it is one of the most widely used and practical methods used in Machine Learning. Contents 1. What is a Decision Tree? 2. Example of a Decision Tree 3. Entropy 4. Information Gain 5. When to stop Splitting? 6. How to stop overfitting? max_depth min_samples_split min_samples_leaf max_features 7. Pruning, Post-pruning Pre-pruning 8. Endnotes What is a Decision Tree? It is a tool that has applications spanning several different areas. Decision trees can be used for classification as well as regression problems. The name itself suggests that it uses a flowchart like a tree structure to show t...