4 Easy Steps for Implementing CatBoost

 By Medium - 2021-03-20

Description

4 easy steps for implementing CatBoost in Python for Data Scientists: Installation, imports, dataset, model, and predict in your Jupyter Notebook.

Summary

  • an end-to-end tutorial on how to apply an emerging Data Science algorithm Table of Contents Introduction Installation and Imports Define Dataset Apply Model Predict Summary References Introduction CatBoost [2] has beaten many other popular machine learning algorithms on benchmark datasets where logloss was the error metric.
  • Also, CatBoost has some awesome plots that visualize the error metric improvements over iterations, so if you want to visually see how the train and test set compare over time, setting the plot to True is a great approach.
  • It really is a powerful machine learning algorithm and library that anyone can implement.
  • Additionally, there are other key visualizations that you can play around with, which are made from the popular SHAP library.

 

Topics

  1. Machine_Learning (0.23)
  2. Backend (0.19)
  3. Database (0.13)

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