# Performance Measures and Evaluations

Learn how to evaluate the performance of our model.

## We'll cover the following

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.

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