Practice Confusion Matrix (4.1) - Classification Algorithms - Data Science Basic

Practice: Confusion Matrix

Lesson 4.1Classification Algorithms

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10 questions

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6 sample questions

Question 1.
What are the four components of a confusion matrix?
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Think of the two types of outcomes versus predictions.
Question 2.
How do you calculate accuracy from a confusion matrix?
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You’re looking for the ratio of correct predictions to all predictions.
3Question 3.
What does a true positive represent in a confusion matrix?
- Correctly predicted positive instances
- Incorrectly predicted positive instances
- Correctly predicted negative instances
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Think about the outcomes when predictions are correct.
4Question 4.
The F1-Score is especially useful when you want to:
- Assess both precision and recall
- Only assess accuracy
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Remember, it's about balancing two important measures.
Question 5.
You analyze a model predicting whether customer service tickets are resolved or not. The confusion matrix shows TP=30, TN=40, FP=10, FN=5. Calculate accuracy, precision, recall, and F1-Score.
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Each metric is derived from the confusion matrix components.
Question 6.
Imagine you're assessing a model that predicts loan defaults. The confusion matrix shows high false positives. Discuss how this affects the bank's strategy, including potential financial impacts.
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Consider the trade-offs between risk management and customer service.

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