Machine Learning Model Selection Bonus

We couldn’t be happier to introduce you to CatBoost, an open-source Machine Learning algorithm that uses gradient boosting over decision trees libraries.

As a self-tuning model, CatBoost allows you to spend less time on parameter tuning, improves your training results and accuracy, among many other benefits.

In this bonus, you will explore CatBoost theory and implement CatBoost on a brain cancer dataset.

This tutorial covers the following steps:

• Importing the libraries
• Importing the dataset 
• Splitting the dataset into the Training and Test set 
• Training CatBoost on the Training set 
• Making the confusion matrix 
• Applying k-fold Cross-Validation

Click here to download all the files you’ll need to implement this project yourself and view the tutorial.