Project
Decision Tree with Pen and Paper
Easy
110 completions
~ 7 hours
4.1Learn how to implement a decision tree, study the math behind it, and build your model. This project requires a basic understanding of probability and the simplest math. As a result, use the decision trees you built to make predictions for new samples.
Provided by
JetBrains Academy
About
Have you ever wondered how machine learning algorithms work from the inside? This project provides a good insight into the mechanism behind one of the well-known algorithms – a decision tree. Classification and calculations, let's do it ourselves!
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Make a simple, intuitive decision tree. Learn how to create simple decision rules.
Learn about the Gini index and data impurity. Find the Gini index for the provided examples.
Apply the Gini index to find the optimal feature and split the data. Build a simple decision tree by repeating the procedure multiple times.
Learn about entropy. Find the entropy of the data.
Grasp the concept of information gain and find out how to use it for finding the optimal splits of the data.
Build another decision tree using information gain. Predict the unclassified data.
You've already built two models; it's time to make predictions about the data.
Reviews
5 days ago
I learned the definition of gini function and entropy and how we can evaluate a gain of information to construct a tree. My biggest challenge is to undestand how every computation must be done to select a feature and so to construct the tree.
Oliver Spollen
2 months ago
I'm not quite sure yet, think I will have to redo the lot to hammer it home! It's a start though.
5 months ago
I have learnt the theory behind decision trees and how to create them myself
4.1
Learners who completed this project within the Introduction to Data Science course rated it as follows: