Practice Lightgbm (5.5.1) - Supervised Learning – Advanced Algorithms - Data Science Advance

Practice: LightGBM

Lesson 5.5.1Supervised Learning – Advanced Algorithms

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9 questions

Flashcard drill
2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is LightGBM primarily used for?
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Think about its primary function in machine learning.
Question 2.
What method does LightGBM use to grow trees?
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Consider how trees are built in this algorithm.
3Question 3.
What is the primary benefit of LightGBM?
- Slower training speed
- Efficient handling of categorical features
- Inability to process large data
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Think about how unique data types can affect model handling.
4Question 4.
True or False: LightGBM uses a level-wise growth strategy for trees.
- True
- False
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Remember how trees are constructed in this framework.
Question 5.
Given a dataset with 1 million entries, how would you approach using LightGBM for analysis? Outline your steps.
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Think about the steps from data loading to model validation.
Question 6.
If you notice LightGBM is overfitting your training data, what strategies could you employ to reduce this?
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Review common solutions for mitigating overfitting in machine learning models.

5 more questions in this section. Enroll free to practise them all.