5.3.3 - Gradient Boosting Machines (GBM)
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is the primary goal of Gradient Boosting Machines?
💡 Hint: Think about how one model can 'learn' from another.
Name a key advantage of using GBM.
💡 Hint: Consider what makes GBM stand out among other methods.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does GBM stand for?
💡 Hint: Focus on the boosting aspect!
True or False: GBM builds decision trees independently.
💡 Hint: Remember the sequential learning process.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
You have a dataset for predicting customer churn in a telecom company. How could you implement GBM here? Discuss considerations regarding overfitting and hyperparameter tuning as part of your approach.
💡 Hint: Think about how you would approach model building and evaluation iteratively.
Given that GBM allows for both L1 and L2 regularization, explain how each could affect your model in a scenario involving a complex feature set with many predictors.
💡 Hint: Consider what happens to model weights under each regularization type.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.