Practice Model Evaluation - 30.4.3 | 30. Introduction to Machine Learning and AI | Robotics and Automation - Vol 2
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30.4.3 - Model Evaluation

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is accuracy in the context of model evaluation?

💡 Hint: Think about how many times the model is correct overall.

Question 2

Easy

Define precision.

💡 Hint: It's about how trustworthy the positive predictions are.

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 the accuracy metric represent?

  • The percentage of correct predictions
  • The number of false predictions
  • Only true positives

💡 Hint: Recall the formula for calculating accuracy.

Question 2

True or False: A higher AUC value indicates that a model performs poorly.

  • True
  • False

💡 Hint: Think about what AUC measures.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a confusion matrix: 

          Predicted Positive  Predicted Negative
Actual Positive       70                   30
Actual Negative       10                   90

Calculate accuracy, precision, recall, and F1-score.

💡 Hint: Use the formulas for these metrics based on the values from the matrix.

Question 2

Discuss the limitations of using only accuracy as a performance metric with examples. Why is it important to consider precision, recall, and F1-score?

💡 Hint: Think about situations like medical diagnoses where false negatives can be life-threatening.

Challenge and get performance evaluation