Practice Step 6: Evaluate The Model (9.7) - Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

Practice: Step 6: Evaluate the Model

Lesson 9.7End-to-End Machine Learning Project – Predicting Student Exam Performance

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9 questions

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4 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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3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define accuracy in the context of model evaluation.
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Think about the total number of cases and correct predictions.
Question 2.
What does precision indicate?
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Focus on true positives versus predicted positives.
3Question 3.
What is a confusion matrix used for?
- To count incorrect predictions
- To assess model performance
- To display training data
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Think about where this matrix is applied.
4Question 4.
True or False: High accuracy always indicates a good model.
- True
- False
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Consider cases where one outcome is much more frequent than the other.
Question 5.
Given a confusion matrix with TP = 30, TN = 50, FP = 10, FN = 5, calculate the accuracy, precision, recall, and F1 score.
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Use the respective formulas for each metric.
Question 6.
Discuss the implications of a model with high accuracy but very low precision. What corrective actions might you take?
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Consider the balance between finding positives and the risk of false positives.

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