# Evaluation Metrics for Classification Problems: Quick Examples + References

Last updated:**True Positives (TP)**: should be TRUE, you predicted TRUE

**False Positives (FP)**: should be FALSE, you predicted TRUE

**True Negative (TN)**: should be FALSE, you predicted FALSE

**False Negatives (FN)**: should be TRUE, you predicted FALSE

All machine learning toolkits provide these model evaluation metrics.

## Metric: Accuracy

"What percentage of my predictions are correct?"

```
Accuracy = (TP + TN) / (TP + FP + TN + FN)
```

Good for single label, binary classifcation.

Not good for imbalanced datasets.

- If, in the dataset, 99% of samples are TRUE and you blindly predict TRUE for everything, you'll have 0.99 accuracy, but you haven't actually learned anything.

## Metric: Precision

"Of the points that I predicted TRUE, how many are actually TRUE?"

```
Precision = TP / (TP + FP)
```

Good for multi-label / multi-class classification and information retrieval

Good for unbalanced datasets

## Metric: Recall

"Of all the points that are actually TRUE, how many did I correctly predict?"

```
Recall = TP / (TP + FN)
```

Good for multi-label / multi-class classification and information retrieval

Good for unbalanced datasets

## Metric: F1

"Can you give me a single metric that balances precision and recall?"

```
F1 = (Precision * Recall) / (Precision + Recall)
```

Gives equal weight to precision and recall

Good for unbalanced datasets

## Metric: AUC (Area under ROC Curve)

"Is my model better than just random guessing?"

- The ROC curve is obtained plotting your model's true-positive and false-positive rates at different points.

*If your model scores less than 0.5 AUC, it's no better than just random guessing.*

Source: http://gim.unmc.edu/dxtests/roc3.htm

Source: http://gim.unmc.edu/dxtests/roc3.htm

- Good for cases when you need to estimate how well your model is at
**discriminating**TRUE from FALSE values.

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### References:

Felipe
TECHNOLOGY

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