https://github.com/bsm/mlmetrics

Common metrics for evaluation of machine learning models
https://github.com/bsm/mlmetrics

classification confusion-matrix golang kappa logloss machine-learning matthews ml online-machine-learning r2 regression

Last synced: about 1 year ago
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Common metrics for evaluation of machine learning models

• Host: GitHub
• URL: https://github.com/bsm/mlmetrics
• Owner: bsm
• License: apache-2.0
• Created: 2018-10-25T14:24:09.000Z (over 7 years ago)
• Default Branch: main
• Last Pushed: 2021-07-20T08:36:04.000Z (almost 5 years ago)
• Last Synced: 2025-03-22T19:02:45.010Z (about 1 year ago)
• Topics: classification, confusion-matrix, golang, kappa, logloss, machine-learning, matthews, ml, online-machine-learning, r2, regression
• Language: Go
• Size: 18.6 KB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ML metrics

[![GoDoc](https://godoc.org/github.com/bsm/mlmetrics?status.svg)](https://godoc.org/github.com/bsm/mlmetrics)

[![Test](https://github.com/bsm/openmetrics/actions/workflows/test.yml/badge.svg)](https://github.com/bsm/openmetrics/actions/workflows/test.yml)

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](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() {

	yTrue := []int{2, 0, 2, 2, 0, 1}

	yPred := []int{0, 0, 2, 2, 0, 2}

	mat := mlmetrics.NewConfusionMatrix()

	for i := range yTrue {

		mat.Observe(yTrue[i], yPred[i])

	}

	// print matrix

	for i := 0; i < mat.Order(); i++ {

		fmt.Println(mat.Row(i))

	}

	// print metrics

	fmt.Println()

	fmt.Printf("accuracy : %.3f\n", mat.Accuracy())

	fmt.Printf("kappa    : %.3f\n", mat.Kappa())

	fmt.Printf("matthews : %.3f\n", mat.Matthews())

}

```