Practice Conclusion (7) - Machine Learning Basics

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11 questions

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6 sample questions

Question 1.
What is supervised learning?
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Think about models that learn from examples.
Question 2.
Define bias in machine learning.
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It's related to underfitting.
3Question 3.
What is the main goal of supervised learning?
- To find patterns in unlabeled data
- To classify data into known categories
- To predict based on labeled data
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Focus on labeled data concepts.
4Question 4.
True or False: Unsupervised learning requires labeled data.
- True
- False
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Remember how data labeling influences the learning type.
Question 5.
You have two models: Model A has low training error but high validation error, while Model B has a higher training error but low validation error. Identify which model is overfitting and which is generalizing well.
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Consider the definitions of overfitting vs. generalization.
Question 6.
Given a dataset with a significant class imbalance, which evaluation metrics would you prioritize, and why?
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How do you measure success in minority classes?

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