# Classification Metrics: Visual Explanations

## Table Of Contents

This post will visually describe the problem of understanding of such concepts as **Accuracy**, **Precision**, **Recall**, **F1-Score**, **ROC Curve**, and **AUC**, which are part of the development of any classification, detection, segmentation, etc. tasks in machine learning. All the images were created by the author.

I would also suggest you read the following articles about those metrics, which are highly informative and could give you a better understanding of the metrics evaluation process:

- Classification: True vs. False and Positive vs. Negative
- Classification: Accuracy
- Classification: ROC Curve and AUC
- Precision and Recall Made Simple
- Evaluating Classification Models: Why Accuracy Is Not Enough
- Precision and Recall: Understanding the Trade-Off
- Essential Things You Need to Know About F1-Score
- Understanding the AUC-ROC Curve in Machine Learning Classification
- Precision & Recall
- ROC & AUC

## The Key Element

The key element of all these metrics is **True Positive (TP)**, **True Negative (TN)**, **False Positive (FP)**, and **False Negative (FN)** metrics, which came from Statistics, specifically from Hypothesis Testing.

**True Positive**is about how many positive samples were classified as positive.**True Negative**is about how many negative samples were classified as negative.**False Positive**is about how many negative samples were classified as positive.**False Negative**is about how many positive samples were classified as negative.

## Accuracy

Accuracy shows **how many correct classifications you have made**.

## Precision

Precision shows **how many positive predictions were correct**.

## Recall

Recall shows **how many predictions were correct across the only positive samples**.

## F1-Score

F1-Score is simply the **harmonic mean between Precision and Recall**.

## ROC Curve

ROC Curve stands for “Receiver Operating Characteristic” and depends on two parameters:

**True Positive Rate (TPR)**, also known as**Recall**.**False Positive Rate (FPR)**, which is the probability of an actual negative class to be predicted as positive.

Using different threshold values from 0 to 1, a ROC Curve is created by plotting FPR values on the X-axis, and TPR values on the Y-axis.

## AUC

AUC stands for “Area under the ROC Curve” and measures the entire two-dimensional **area underneath the entire ROC curve from (0,0) to (1,1)**.

## Conclusion

These metrics are widely used in different machine learning topics, so it is required to get a clear intuition about how they work, how to interpret, and, finally, how to raise them to 100%.