## Performance metrics for classification

To understand the concepts easily, let's take the case of binary classification, where the task is to classify an input feature vector into one of the two states: -1 or 1. Assume that 1 is the positive class and -1 is the negative class. The predicted output contains only -1 or 1, but there can be two types of errors. Some of the -1 in the test set could be predicted as 1. This is called a **false positive or type I** error. Similarly, some of the 1 in the test set could be predicted as -1. This is called a
**false negative or type II** error. These two types of errors can be represented in the case of binary classification as a confusion matrix as shown below.

Confusion Matrix |
Predicted Class | ||
---|---|---|---|

Positive |
Negative | ||

Actual Class |
Positive |
TP |
FN |

Negative |
FP |
TN |

From the confusion matrix, we can derive the following performance metrics:

**Precision**: This gives the percentage of correct answers in the output predicted as positive**Recall**: This gives the percentage...