Evaluating Performance -Classification

We feed the test image to the trained model, compares the predicted output with test image's label to evaluate either it's correct or wrong prediction. At the end, we will have the count of correct matches and the incorrect matches. The key realization we need to make, is that in the real world not all incorrect and correct matches hold equal value. Also in the real world, a single metric won't tell the complete story, that's why previously mentioned four metrics are used to evaluate the model. We could organize our predicted values compared to the real values in a confusion matrix.