Practice Modeling (1.4.5) - Introduction to Data Science

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9 questions

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4 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 modeling in data science?
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Think of how predictions are made from data.
Question 2.
Name one type of machine learning algorithm.
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Consider what algorithms are used for predicting outcomes.
3Question 3.
What does modeling involve?
- Creating predictive models
- Collecting data
- Cleaning data
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Think about which phase directly uses algorithms.
4Question 4.
True or False: Overfitting indicates a model is generalizing well to new data.
- True
- False
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Think about what happens when a model focuses too much on the training set.
Question 5.
You have trained a model, but your validation accuracy is significantly lower than training accuracy. What steps could you take to address this issue?
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Think about how you could create a more flexible or generalized model.
Question 6.
Create a balanced dataset for training a classification model. How would you approach this, and what techniques might you use?
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Consider ways to adjust your data rather than just throwing out data.

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