True Positives vs False Positives and True Negatives vs False Negatives | Model Evaluation Metrics

True Positives and True Negatives are correctly predicted observations and are therefore shown in GREEN.

We want to minimize False Positives and False Negatives so that they are displayed in RED.


True positive (TP): These are positive values for a correct prediction, which means that the value of the actual class is YES, and the value of the predicted class is also YES.

True Negative (TN): These are negative values that are correctly predicted, which means that the value of the actual class is NO and the value of the predictive class is also NO

False Positive (FP): When the actual class is NO and the predictive class is YES

False negative (FN): when the actual class is YES but the predicted class is NO


Note: False Positives and False Negatives, these values occur when your actual class conflicts with the predictive class.


ROC Curve

The true positive rate gives the proportion of actual positives which are correctly identified as such. (Higher is better)

The false positive rate gives the proportion of falsely identified positives amongst all actual negatives. (lower is better)