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3 cards from this section. Quick, good the night before a test.
What is a confusion matrix?
Think of it as a report card for classifications.
Define Overall Accuracy.
Consider the ratio of correct predictions to total predictions.
What does a confusion matrix help assess?
Think about what kind of assessments we want to make about our classifications.
True or False: User's Accuracy measures the probability of reference pixels being classified correctly.
Remember whose perspective it matters from.
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.
Make sure to follow each step carefully.
Discuss the implications of a low Kappa Coefficient value in terms of model reliability and classification performance.
Reflect on the importance of trust in the results attained from satellite imagery.
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