Practice Model Evaluation Metrics (8) - Chapter 8: Model Evaluation Metrics

Practice: Model Evaluation Metrics

Lesson 8Model Evaluation Metrics

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10 questions

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6 sample questions

Question 1.
Define accuracy in your own words.
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Think about the formula for accuracy.
Question 2.
What does TP stand for in a confusion matrix?
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It's part of the metrics to assess positive predictions.
3Question 3.
What does the confusion matrix specifically display?
- True positives and true negatives only
- True and false positives and negatives
- Only accuracy metrics
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It provides a detailed breakdown of model predictions.
4Question 4.
True or False: AROC AUC score of 0.5 indicates a perfect model.
- True
- False
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Think about the meaning of AUC in terms of performance.
Question 5.
Given a dataset of 100 samples with the following results: TP=30, TN=50, FP=10, FN=10, calculate the accuracy, precision, recall, F1 score, and AUC.
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Use the formulas discussed!
Question 6.
In a scenario where you have a model that predicts a highly imbalanced dataset (95% negative class), suggest how you would evaluate the model effectively.
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Consider metrics that reflect both classes' performances.

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