Practice Step 4: Build The Model – Logistic Regression (9.5) - Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

Practice: Step 4: Build the Model – Logistic Regression

Lesson 9.5End-to-End Machine Learning Project – Predicting Student Exam Performance

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9 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is logistic regression primarily used for?
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Think about what type of results logistic regression returns.
Question 2.
What method is used to fit a logistic regression model in Python?
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This method is commonly used in machine learning to train models.
3Question 3.
What does logistic regression predict?
- Continuous values
- Binary outcomes
- Both continuous and binary outcomes
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Remember what kind of outcomes we discussed.
4Question 4.
True or False: Logistic regression can only handle linear relationships.
- True
- False
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Think about the flexibility of logistic regression.
Question 5.
If a logistic regression model predicts a 0.65 chance of passing for a student with 3 study hours and 70% attendance, what strategies would you recommend for this student? Provide at least three.
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Think about the inputs the model suggests improving.
Question 6.
Create a scenario with three different student profiles that might yield different probabilities using logistic regression. What outcomes would you expect based on varying study hours and attendance?
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Focus on how input variations affect the predicted probabilities.

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