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Comparing Classification Model Quality

Stephanie Glen looks at ways to compare model evaluation for classification models:

In part 1, I compared a few model evaluation techniques that fall under the umbrella of ‘general statistical tools and tests’. Here in Part 2 I compare three of the more popular model evaluation techniques for classification and clustering: confusion matrix, gain and lift chart, and ROC curve. The main difference between the three techniques is that each focuses on a different type of result:

– Confusion matrix: false positives, false negatives, true positives and true negatives.
– Gain and lift: focus is on true positives.
– ROC curve: focus on true positives vs. false positives.

These are good tools for evaluation and Stephanie does a good job explaining each.