Meet CatBoost — the gradient-based model that is bound to beat out the power that LightGBM (a gradient-based model that uses tree-based learning algorithms) has to offer on specific problems.

CatBoost is a great self-tuning model to have in the toolkit whenever you want to get the highest accuracy on datasets that have many categorical features, which is usually the case with on-the-job problems.

In this bonus, you will explore CatBoost theory and implement CatBoost on the brain cancer dataset (the same dataset we used for XGBoost).

This tutorial covers the following steps:

• Importing the libraries

• Importing the dataset

• Splitting the dataset into the Training and Test set

• Training XGBoost on the Training set

• Making the confusion matrix

• Applying k-fold Cross-Validation

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