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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a confusion matrix?
💡 Hint: Think of it as a report card for classifications.
Question 2
Easy
Define Overall Accuracy.
💡 Hint: Consider the ratio of correct predictions to total predictions.
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 does a confusion matrix help assess?
💡 Hint: Think about what kind of assessments we want to make about our classifications.
Question 2
True or False: User's Accuracy measures the probability of reference pixels being classified correctly.
💡 Hint: Remember whose perspective it matters from.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
Given a confusion matrix for a land cover classification with the following data: True Positive (TP) = 30, False Positive (FP) = 10, True Negative (TN) = 40, False Negative (FN) = 5. Compute Overall Accuracy, User's Accuracy, Producer's Accuracy, and Kappa Coefficient.
💡 Hint: Make sure to follow each step carefully.
Question 2
Discuss the implications of a low Kappa Coefficient value in terms of model reliability and classification performance.
💡 Hint: Reflect on the importance of trust in the results attained from satellite imagery.
Challenge and get performance evaluation