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. That said, you'll want to choose a method that gives you the answers you need for the particular field you're in. For example, while a confusion matrix can be a great tool for comparing models, it isn't much good for marketing decisions (where the gain and lift chart would be a better choice). Other less popular (but still valid) tools include the K-S chart and Gini Coefficient.