Desktop Survival Guide
by Graham Williams
True positives (TPs) are those entities which are correctly classified by a model as positive instances of the concept being modelled (e.g., the model identifies them as a case of fraud, and they indeed are a case of fraud). False positives (FPs) are classified as positive instances by the model, but in fact are known not to be. Similarly, true negatives (TNs) are those entities correctly classified by the model as not being instances of the concept, and false negatives (FNs) are classified as not being instances, but are in fact know to be. These are the basic measures of the performance of a model. These basic measures are often presented in the form of a confusion matrix, produced using a contingency table.