CatBoost vs XGBoost – Quick Intro and Modeling Basics
CatBoost vs XGBoost – Quick Intro and Modeling Basics, available at Free, has an average rating of 4.8, with 6 lectures, based on 194 reviews, and has 5746 subscribers.
You will learn about Learn how to use CatBoost in regression and classification with Python This course is ideal for individuals who are Beginning data scientists It is particularly useful for Beginning data scientists.
Enroll now: CatBoost vs XGBoost – Quick Intro and Modeling Basics
Summary
Title: CatBoost vs XGBoost – Quick Intro and Modeling Basics
Price: Free
Average Rating: 4.8
Number of Lectures: 6
Number of Published Lectures: 6
Number of Curriculum Items: 6
Number of Published Curriculum Objects: 6
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Learn how to use CatBoost in regression and classification with Python
Who Should Attend
- Beginning data scientists
Target Audiences
- Beginning data scientists
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!
Course Curriculum
Chapter 1: CatBoost vs XGBoost – A CatBoost Introduction
Lecture 1: Introduction
Lecture 2: CatBoost vs XGBoost Battle 1
Lecture 3: CatBoost vs XGBoost Battle 2
Lecture 4: CatBoost vs XGBoost Battle 3
Lecture 5: Let's look at CatBoostClassifier and CatBoostRegressor
Lecture 6: Conclusion
Instructors
-
Manuel Amunategui
Data Scientist & Quantitative Developer
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 4 votes
- 3 stars: 13 votes
- 4 stars: 61 votes
- 5 stars: 114 votes
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