https://github.com/pharo-ai/metrics

Implementation of Machine Learning metrics for Pharo
https://github.com/pharo-ai/metrics

adjusted-rand-index fowlkes-mallow jaccard machine-learning pharo pharo-smalltalk randindex

Last synced: 25 days ago
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Implementation of Machine Learning metrics for Pharo

• Host: GitHub
• URL: https://github.com/pharo-ai/metrics
• Owner: pharo-ai
• License: mit
• Created: 2020-10-05T04:39:37.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-12-19T14:43:26.000Z (about 1 year ago)
• Last Synced: 2024-10-29T20:32:41.590Z (2 months ago)
• Topics: adjusted-rand-index, fowlkes-mallow, jaccard, machine-learning, pharo, pharo-smalltalk, randindex
• Language: Smalltalk
• Homepage:
• Size: 89.8 KB
• Stars: 2
• Watchers: 5
• Forks: 0
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Description

This package is part of the Pharo AI project: It contains implementations, tests and documentation of different metrics for Machine Learning models. The evaluation metrics allows to assess how a trained Machine Learning model has performed.

For more information please refer to the wiki: https://github.com/pharo-ai/wiki/blob/master/wiki/DataExploration/Metrics.md

There is explained with more detail the available metrics and how to use them.

## How to install it

```smalltalk

EpMonitor disableDuring: [ 

   Metacello new

      baseline: 'AIMetrics';

      repository: 'github://pharo-ai/metrics';

      load ]

```

## How to depend on it

```smalltalk

spec 

   baseline: 'AIMetrics' 

   with: [ spec repository: 'github://pharo-ai/metrics' ].

```

Types of metrics: 

- Clustering metrics

- Regression metrics

- Classification metrics

### Example: Mean Squared Error (`AIMeanSquaredError`)

```st

| yTrue yPredicted metric |

metric := AIMeanSquaredError new.

yTrue := #( 3 -0.5 2 7 ).

yPredicted := #( 2.5 0.0 2 8 ).

metric computeForActual: yTrue predicted: yPredicted "0.375"

```

### Example: Accuracy Score (`AIAccuracyScore`)

```st

| yTrue yPredicted metric |

metric := AIAccuracyScore new.

yTrue := #( 0 1 2 3 ).

yPredicted := #( 0 2 1 3 ).

metric computeForActual: yTrue predicted: yPredicted "0.5"

```