Practice Gradient Boosting Machines (gbm) (5.3.3) - Supervised Learning – Advanced Algorithms

Practice: Gradient Boosting Machines (GBM)

Lesson 5.3.3Supervised Learning – Advanced Algorithms

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10 questions

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5 cards from this section. Quick, good the night before a test.
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6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the primary goal of Gradient Boosting Machines?
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Think about how one model can 'learn' from another.
Question 2.
Name a key advantage of using GBM.
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Consider what makes GBM stand out among other methods.
3Question 3.
What does GBM stand for?
- Gradient Bigger Machines
- Gradient Boosting Machines
- Generative Boosting Models
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Focus on the boosting aspect!
4Question 4.
True or False: GBM builds decision trees independently.
- True
- False
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Remember the sequential learning process.
Question 5.
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.
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Think about how you would approach model building and evaluation iteratively.
Question 6.
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.
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Consider what happens to model weights under each regularization type.

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