Practice Xgboost, Lightgbm, Catboost (modern Boosting Powerhouses) (4.4.3) - Advanced Supervised Learning & Evaluation (Weeks 7)
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Engineering Courses
Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Certifications
Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

XGBoost, LightGBM, CatBoost (Modern Boosting Powerhouses)

Practice - XGBoost, LightGBM, CatBoost (Modern Boosting Powerhouses)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does XGBoost stand for?

💡 Hint: Think about how it enhances gradient boosting.

Question 2 Easy

List one advantage of using LightGBM.

💡 Hint: Consider its unique tree growth strategy.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary advantage of using XGBoost?

Very slow
High speed and performance
Complex to use

💡 Hint: Consider the features that make it stand out.

Question 2

True or False: LightGBM uses a breadth-first strategy for tree growth.

True
False

💡 Hint: Think about how trees can be built differently.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a simple flowchart showing when to use XGBoost, LightGBM, and CatBoost based on the nature of data (structured, large datasets, categorical features).

💡 Hint: Consider what makes each suited for specific dataset challenges.

Challenge 2 Hard

Critique the advantages and disadvantages of using XGBoost compared to traditional gradient boosting algorithms. Provide examples of when each would be appropriate.

💡 Hint: Think in terms of model complexity versus user-friendliness.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.