# Classification model

> A model whose prediction is a class. For example, the following are all classification models:[^1]

A [model](https://wiki.g15e.com/pages/Model%20(machine%20learning.txt)) whose [prediction](https://wiki.g15e.com/pages/Prediction%20(machine%20learning.txt)) is a [class](https://wiki.g15e.com/pages/Class%20(machine%20learning.txt)). For example, the following are all classification models:[^1]

- A model that predicts an input sentence's language (French? Spanish? Italian?).
- A model that predicts tree species (Maple? Oak? Baobab?).
- A model that predicts the positive or negative class for a particular medical condition.

## Types of classification model

- [Binary classification](https://wiki.g15e.com/pages/Binary%20classification.txt)
- <Multi-class classification>

## Metrics

[Accuracy](https://wiki.g15e.com/pages/Accuracy%20(machine%20learning.txt)):

- Use as a rough indicator of model [training](https://wiki.g15e.com/pages/Training%20(machine%20learning.txt)) progress/[convergence](https://wiki.g15e.com/pages/Convergence%20(machine%20learning.txt)) for balanced datasets.
- For model performance, use only in combination with other metrics.
- Avoid for [imbalanced datasets](https://wiki.g15e.com/pages/Class-imbalanced%20dataset.txt). Consider using another metric.

[Recall](https://wiki.g15e.com/pages/Recall%20(machine%20learning.txt)):

- Use when [false negative](https://wiki.g15e.com/pages/False%20negative.txt) are more expensive than [false positives](https://wiki.g15e.com/pages/False%20positive.txt).

[False positive rate](https://wiki.g15e.com/pages/False%20positive%20rate.txt):

- Use when [false positives](https://wiki.g15e.com/pages/False%20positive.txt) are more expensive than [false negatives](https://wiki.g15e.com/pages/False%20negative.txt).

[Precision](https://wiki.g15e.com/pages/Precision%20(machine%20learning.txt)):

- Use when it's very important for positive predictions to be accurate.

## See also

- [Regression models](https://wiki.g15e.com/pages/Regression%20model.txt) predict numbers rather than classes.
- [ML crash course - Classification](https://wiki.g15e.com/pages/ML%20crash%20course%20-%20Classification.txt)

## Footnotes

[^1]: https://developers.google.com/machine-learning/glossary#classification_model