Practice Evaluation Metrics - 6.3.2 | Machine Learning Basics | AI Course Fundamental
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6.3.2 - Evaluation Metrics

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define 'accuracy' in the context of classification metrics.

💡 Hint: Think about the correct predictions made by a model.

Question 2

Easy

What does the confusion matrix display?

💡 Hint: Visualize how a model performs on different classifications.

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 is precision in classification metrics?

  • Ratio of true positives to total positives
  • Ratio of true positives to predicted positives
  • Ratio of true negatives to total outcomes

💡 Hint: Think about false positives in the definition.

Question 2

True or False: Mean Squared Error is always less than Mean Absolute Error for any dataset.

  • True
  • False

💡 Hint: Consider the nature of squaring the errors.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

A telecom company wants to assess if their model for predicting customer churn is effective. After implementing the model, the confusion matrix reveals 70 true positives (churn correctly predicted), 30 false positives (predicted churn but retained), 10 false negatives (churn not predicted), and 90 true negatives (correct retention). Calculate accuracy, precision, recall, and F1 Score.

💡 Hint: Make sure to apply the formulas for each metric.

Question 2

Imagine you are tasked with modeling housing prices using regression metrics. If your predictions yield an MAE of 2000 and an MSE of 5000000, illustrate why it is beneficial to check both metrics rather than relying on just one.

💡 Hint: Think of the implications of errors in pricing and their impact on business.

Challenge and get performance evaluation