**Recall, Precision, F1 Score** how to easily remember their usefulness and what these metrics imply ?

To understand these metrics, you need to know the concepts of **True Positive / False Negative** (detailed in this article along with a method to not confuse them).

From these concepts, we will deduce metrics that will allow us **to better analyze the performance of our Machine Learning model !**

## Recall

**Recall** gives us **the percentage of positives well predicted by our model.**

In other words, it is **the number of well predicted positives** (True Positive)** divided by the total number of positives** (True Positive + False Negative).

In **mathematical** terms, it gives us :

But what is **the point of recall? **

The **higher** it is, the more the Machine Learning model **maximizes the number of True Positives.**

Bu**t be careful**, this does not mean that the model isn’t wrong.

When the recall is **high**, it rather means that **it will not miss any positive**. Nevertheless it doesn’t give any information about its prediction quality on the negatives.

## Precision

**Precision** is quite similar to recall, so it is important to understand the difference.

It shows **the number of positive predictions well made.**

In other words, it is **the number of well predicted positives** (True Positive) **divided by all the positives predicted **(True Positive + False Positive).

This gives us in **mathematical** terms :

What is **the benefit of precision ?**

The **higher** it is, the more the Machine Learning model **minimizes the number of False Positives.**

When the precision is **high**, it means that **the majority of the positive predictions of the model are well predicted positives.**

## A concrete example to better understand

Let’s go back to **our example** from the article on True Positives and False Negatives.

We are **in front of a tunnel** and we have to **predict whether a car** (Positive) **or a motorcycle** (Negative) will come out.

Well, **recall** is the number of cars that our model predicted, and that turned out to be cars, divided by the total number of cars that went through the tunnel.

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**Precision** is also the number of cars that our model predicted, and that turned out to be cars, but in this case, divided by the total number of cars that our model predicted, and that turned out to be true (car) or false (motorcycle).

In fact with the recall, we look at the number of positives that the model has predicted well on all the positives.

Whereas with precision, we look at **the number of positives that the model has predicted on the set of positives predicted.**

## How to remember ?

**In one sentence :**

- The higher the recall, the more positives the model finds
- The higher the precision, the less the model is wrong on the positives

## F1 Score

Although useful, neither precision nor recall can fully **evaluate a Machine Learning model.**

Separately these two metrics are **useless** :

- if the model
**always**predicts “positive”, r**ecall will be high** - on the contrary, if the model
**never**predicts “positive”,**the precision will be high**

We will therefore have metrics that **indicate** that our model is **efficient** when it is, on the contrary, more **naive** than intelligent.

**Fortunately for us**, a metric exists to combine precision and recall : **F1 Score.**

The F1 Score provides **a good evaluation of the performance of our model.**

It is calculated as follows :

So why calculate **F1 Score** and not just the **average** of the two metrics ?

In fact, in statistics, the calculation on **percentages** is not exactly the same as on **integers.**

Here the **F1 Score** is what we call the **harmonic mean**. It is another type of average than the usual one and it is an excellent way to calculate **the average of rate or percentage** (here recall and precision).

This makes the F1 Score **one of the most used metrics** among Data Scientists !

As you might have **understood**, the **higher** your F1 Score, the **better** your model will perform.

That’s it for this article on **Machine Learning** metrics, I hope it was **useful for you** 😀

**sources :**

- L. Antiga,
*Deep Learning with PyTorch*(2020, Manning Publications) – our affiliated link - Photo by Jonathan Körner on Unsplash

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**BEWARE**, this email series is not for everyone. If you are the kind of person who likes theoretical and academic courses, you can skip it.

But if you want to learn **the PANE method** to do Deep Learning, **click here** :