Performance Measures and Evaluations

We used the percentage of misclassification as an objective function to evaluate the performance of the model. This is a common choice and often a good start in our examples, but there are other commonly used evaluation measures that we should understand. Let’s consider first a binary classification case where it is common to call one class positive and the other class negative. This nomenclature comes from diagnostics, such as trying to decide if a person has a disease based on some clinical tests. We can then define the following four performance indicators:

• True Positive (TP): The number of correctly predicted positive samples.
• True Negative (TN): The number of correctly predicted negative samples.
• False Positive (FP): The number of incorrectly predicted positive samples.
• False Negative (FN): The number of incorrectly predicted negative samples.

Confusion matrix

These numbers are often summarized in a confusion matrix, and such a matrix layout is shown in the figure below.