Learn How to Apply Tree Based Algorithms like Decision Trees and Random Forest!

Tree based algorithms are considered to be one of the best and mostly used supervised learning methods in the machine learning space. These tree based algorithms empower predictive models with high accuracy, stability and ease of interpretation. Unlike linear models, they map nonlinear relationships quite well. They are adaptable at solving any kind of problem we encounter (classification or regression).

This comprehensive ebook will help beginners learn tree based algorithms from scratch. You can expect to have a solid understanding and grasp of tree based algorithms by the end of the book, and will be ready to build predictive models!

What’s Included in the Tree-Based Algorithms Book?

This book covers a broad range of topics related to tree-based modeling, including:

  • What is a Decision Tree? How does it work?

  • Regression Trees vs Classification Trees

  • How does a tree based algorithm decide where to split?

  • What are the key parameters of model building and how can we avoid over-fitting in tree based algorithms?

  • Are tree based algorithms better than linear models?

  • Working with Decision Trees in R and Python

  • What is Ensemble Modeling in Machine Learning?

  • What are Bagging and Boosting?

  • Deep dive into Random Forest

  • Gradient Boosting vs. XGBoost - Which algorithm should you choose?

Who Should Read the Comprehensive Ebook on Tree-Based Algorithms Book?

This book is for anyone who:

  • Wants to learn tree-based algorithms like decision trees and random forest

  • Wants to advanced their machine learning career

  • Is curious about the most popular machine learning algorithms

  • Is a beginner in machine learning and tree-based algorithms

  • Is curious about how to build advanced tree-based models

Should you Know Any Machine Learning Before Picking up this Book?

We have written this book for beginners so no prior knowledge of machine learning is required. However, if you wish to implement the tree-based models we've covered in the book, an elementary knowledge of Python or R would be helpful.


We have included both Python and R code in the book itself so you can follow along as you read.

Author of the Book

  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.