Python Decision Tree and Random Forest

Decision Tree

A Decision Tree is one of the popular and powerful machine learning algorithms that I have learned. The basics of Decision Tree is explained in detail with clear explanation.

I have given complete theoritical stepwise explanation of computing decision tree using ID3 (Iterative Dichotomiser) and CART (Classification And Regression Trees) along sucessfully implemention of decision tree on ID3 and CART using Python on playgolf_data and Iris dataset

Play Golf dataset:

ID3 dataset analysis	CART dataset analysis

Iris dataset

1. Method 1: Print Text Representation

2. Method 2: Plot Tree with plot_tree

3. Method 3: Plot Decision Tree with graphviz

4. Method 4: Plot Decision Tree with dtreeviz Package

5. Method 5: Visualizing the Decision Tree in Regression Task

Table of contents 📋

These are online read-only versions. However you can Run ▶ all the codes online by clicking here ➞

Frequently asked questions ❔

How can I thank you for writing and sharing this tutorial? 🌷

You can and Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click ➞ ✰ Star and ⵖ Fork button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

How can I read this tutorial without an Internet connection?

1. Go here and click the big green ➞ Code button in the top right of the page, then click ➞ Download ZIP.

   

2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

   Kernel > Restart & Clear Output

This will clear all the outputs and now you can understand each statement and learn interactively.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.

Authors ✍️

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome 🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a ⭐ star.

Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.