Practice Model Evaluation (30.4.3) - Introduction to Machine Learning and AI

Practice: Model Evaluation

Lesson 30.4.3Introduction to Machine Learning and AI

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10 questions

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5 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
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is accuracy in the context of model evaluation?
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Think about how many times the model is correct overall.
Question 2.
Define precision.
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It's about how trustworthy the positive predictions are.
3Question 3.
What does the accuracy metric represent?
- The percentage of correct predictions
- The number of false predictions
- Only true positives
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Recall the formula for calculating accuracy.
4Question 4.
True or False: A higher AUC value indicates that a model performs poorly.
- True
- False
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Think about what AUC measures.
5Question 5.
Given a confusion matrix:
Python
Predicted Positive Predicted Negative Actual Positive 70 30 Actual Negative 10 90
Calculate accuracy, precision, recall, and F1-score.
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Use the formulas for these metrics based on the values from the matrix.
Question 6.
Discuss the limitations of using only accuracy as a performance metric with examples. Why is it important to consider precision, recall, and F1-score?
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Think about situations like medical diagnoses where false negatives can be life-threatening.

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