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UdemyCatBoost vs XGBoost - Quick Intro and Modeling Basics
4.7(216)6k studentsINTERMEDIATEEnglish
Korshub's take
CatBoost Meets XGBoost: A Fast, Hands-On Comparison
Rather than teaching either algorithm in isolation, this course puts CatBoost head-to-head against XGBoost so you can see how each performs in practice using Python. It's built around two compact, dataset-driven parts rather than abstract theory.
What You'll Build
- Part 1: both models are applied to the Titanic dataset — the ship's manifest of who survived and who didn't — a well-known, highly predictive dataset where patterns like age, gender, and class strongly influenced outcomes
- Part 2: a linear regression and classification pass on the Titanic data, plus a modeling pass on the Boston housing dataset, along with an introduction to Pandas Profiler for fast exploratory data analysis
The course closes by pointing you toward Kaggle competitions as the next step — a nudge to apply what you've learned against real, evolving datasets rather than stopping at the tutorial.
Who it's for: learners already comfortable with basic machine learning concepts who want a quick, practical comparison of two leading boosted-tree libraries rather than a from-scratch course.
About this course
XGBoost is one of the most powerful boosted models in existence until now... here comes CatBoost. Let's explore how it compares to XGBoost using Python and also explore CatBoost on both a classification dataset and a regression one. Let's have some fun!
Part 1
We're going to start by unleashing XGBoost and CatBoostost on an independent data set version of the Titanic - the ship's manifest of those that did and didn't survive the tragic sinking of the ship in the North Atlantic Ocean. It happened in 1912 after hitting an iceberg on its maiden voyage to New York. You probably have already used it as it is extremely predictive, basically, women, children and the rich survived while men and the poor mostly didn't.
Part 2
In the second part, we'll model a linear regression and classification on the titanic for classification and the Boston housing data.I'll also introduce you to a cool tool - Pandas Profiler for quick EDAs.
Please go out and use this model on a Kaggle competition, get an account if you haven't already and experiment - sometimes follow the rules, sometimes, don't. Remember that data science is very new so we're still inventing things as we go, just like these new models allow us to explore a little further and further each time!
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