Practice Classification Metrics - 12.2.A | 12. Model Evaluation and Validation | Data Science Advance
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12.2.A - Classification Metrics

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does accuracy measure in classification?

💡 Hint: Think about how we count both true positives and true negatives.

Question 2

Easy

What is precision?

💡 Hint: It indicates how many of the positive predictions were correct.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does accuracy indicate in a classification model?

  • Correct predictions
  • True Positives only
  • True Negatives only

💡 Hint: It combines both correct classifications.

Question 2

True or False: High accuracy always means a good model.

  • True
  • False

💡 Hint: Consider what happens with skewed class distributions.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A fraud detection model predicts cases as fraudulent or non-fraudulent. The model results showed 90 true positives, 5 false positives, 10 false negatives, and 95 true negatives. Calculate the accuracy, precision, recall, and F1-score.

💡 Hint: Use the formulas carefully and ensure you tally all components from confusion matrix correctly.

Question 2

Consider a predictive maintenance model where high recall is desired to not miss any potential machine failures. If the model predicts 80% of actual fails but has a precision of only 30%, discuss the implications of these results.

💡 Hint: Reflect on how balancing these metrics impacts operational costs and efficiencies in real-world applications.

Challenge and get performance evaluation