Practice Metrics - 4.2 | Classification Algorithms | Data Science Basic
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4.2 - Metrics

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does TP stand for in the context of a confusion matrix?

💡 Hint: Think about how many times we correctly predicted the positive class.

Question 2

Easy

How would you calculate accuracy from a confusion matrix?

💡 Hint: Remember to add true positives and true negatives.

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 a True Positive indicate?

  • The model predicted a negative case correctly
  • The model predicted a positive case correctly
  • The model predicted all cases as positive

💡 Hint: Think about what the 'True' refers to in this context.

Question 2

Is a low F1-score an indication of poor model performance?

  • True
  • False

💡 Hint: Consider the relationship between precision and recall.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a dataset where 100 instances were tested. The confusion matrix shows TP=70, FP=10, TN=15, FN=5. What are the accuracy, precision, recall, and F1-score?

💡 Hint: Break down each metric using the confusion matrix values.

Question 2

Propose a strategy to increase the precision of a model that has low precision currently while ensuring not to severely sacrifice recall.

💡 Hint: Think about what kind of adjustments help in precision without completely missing too many actual positive cases.

Challenge and get performance evaluation