Accuracy, Precision, Recall & F1-Score are widely used in machine learning. In this tutorial, we will discuss how to compute them.

In order to understand them easily, we should notice a confusion matrix.

For example:

Here:

TP (True Positive): positive samples are predicted to positive correctly.

FN (False Positive): positive samples are predicted to negative wrongly.

FP (Fasle Negative): negative samples are predicted to postive wrongly.

TN (True Negative): negative samples are predicted to negative correctly.

Accuracy, Precision, Recall & F1-Score can be computed as follows:

## Accuracy

As to accuracy, it is easy to understand, we can understand it as below:

## Precision

Precision amis to positive prediction. We can understand it as follows:

It is computed as:

TP / (TP + negative samples are predicted to positive wrongly)

We can find:

Precision determines the ability to classify negative samples.

## Recall

Recall can be understand as follows:

Recall can be computed as:

TP / (TP + positive samples are predicted to negative wrongly)

We can find:

Recall determines the ability to classify positive samples.

## When use Precision and Recall?

If the negative samples are important, we should focus on precision. Otherwise, we should focus on recall.

However, as to F1-score, the value higher, the model is better.