Practice Evaluating Classification Models (4) - Classification Algorithms

Practice: Evaluating Classification Models

Lesson 4Classification Algorithms

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10 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
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does True Positive mean in a confusion matrix?
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Think about what 'true' indicates in terms of predictions.
Question 2.
Define Accuracy in terms of classification performance.
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It's a simple formula involving TP and TN.
3Question 3.
What does a True Positive indicate in the context of a confusion matrix?
- Correctly predicted negative instances
- Correctly predicted positive instances
- Incorrectly predicted positive instances
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Focus on what 'True' and 'Positive' mean in this context.
4Question 4.
True or False: F1-Score is calculated as the average of Precision and Recall.
- True
- False
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Recall the specific formula for F1-Score.
Question 5.
You have a confusion matrix showing TP=90, FP=30, TN=50, and FN=10. Discuss the implications of these values on model performance and calculate the Precision, Recall, and F1-Score.
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Apply formulas directly and consider real-world consequences.
Question 6.
Consider a scenario where the confusion matrix reveals a high accuracy but low recall. What might this indicate about the model, and how could it be adjusted?
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Backtrack through your definitions of precision and recall to connect them.

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