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Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define accuracy in the context of model evaluation.
💡 Hint: Think about the total number of cases and correct predictions.
Question 2
Easy
What does precision indicate?
💡 Hint: Focus on true positives versus predicted positives.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is a confusion matrix used for?
💡 Hint: Think about where this matrix is applied.
Question 2
True or False: High accuracy always indicates a good model.
💡 Hint: Consider cases where one outcome is much more frequent than the other.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
Given a confusion matrix with TP = 30, TN = 50, FP = 10, FN = 5, calculate the accuracy, precision, recall, and F1 score.
💡 Hint: Use the respective formulas for each metric.
Question 2
Discuss the implications of a model with high accuracy but very low precision. What corrective actions might you take?
💡 Hint: Consider the balance between finding positives and the risk of false positives.
Challenge and get performance evaluation