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...