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README.md.tpl
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# ML metrics
[](https://godoc.org/github.com/bsm/mlmetrics)
[](https://github.com/bsm/openmetrics/actions/workflows/test.yml)
[](https://opensource.org/licenses/Apache-2.0)
Common metrics for evaluation of machine learning models.
Goals:
* Fast!
* Thread-safe
* Support for online evaluation
## Supported Metrics
Classification:
* [Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision)
* [Confusion Matrix](https://en.wikipedia.org/wiki/Confusion_matrix)
* [F1 Score](https://en.wikipedia.org/wiki/F1_score)
* [Kappa](https://en.wikipedia.org/wiki/Cohen%27s_kappa)
* [Matthews](https://en.wikipedia.org/wiki/Matthews_correlation_coefficient)
* [LogLoss](https://en.wikipedia.org/wiki/Loss_functions_for_classification)
* [Precision](https://en.wikipedia.org/wiki/Information_retrieval#Precision)
* [Sensitivity](https://en.wikipedia.org/wiki/Sensitivity_(test))
Regression:
* [Mean Absolute Error](https://en.wikipedia.org/wiki/Mean_absolute_error)
* [Mean Squared Error](https://en.wikipedia.org/wiki/Mean_squared_error)
* [Root Mean Squared Error](https://en.wikipedia.org/wiki/Root-mean-square_deviation)
* [Mean Squared Error](https://en.wikipedia.org/wiki/Root-mean-square_deviation)
* [R²](https://en.wikipedia.org/wiki/Coefficient_of_determination)
## Documentation
Documentation and example are available via godoc at http://godoc.org/github.com/bsm/mlmetrics
## Example
```go
package main
import (
"github.com/bsm/mlmetrics"
)
func main() {{ "ExampleConfusionMatrix" | code }}
```