4 Easy Steps for Implementing CatBoost

CatBoost [2] has beaten many other popular machine learning algorithms on benchmark datasets where logloss was the error metric. It beat mainly LightGBM and XGBoost, which have recently been the standard before in not only data science competitions, but also in professional settings as well. Now is the time to learn this powerful library, and below is how you can implement it in four easy steps. This tutorial will be using popular data science tools like Python and Jupyter Notebook. First, we will start off with the three simple installation commands, then move on to all the necessary imports that you would need for your first, basic CatBoost regression model -- which, as you will see, might be your first and last, because that is how impressively great CatBoost is without much tuning or additional code.