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The evaluation metrics allows to assess how a trained Machine Learning model has performed.\n\nFor more information please refer to the wiki: https://github.com/pharo-ai/wiki/blob/master/wiki/DataExploration/Metrics.md\n\nThere is explained with more detail the available metrics and how to use them.\n\n## How to install it\n\n```smalltalk\nEpMonitor disableDuring: [ \n   Metacello new\n      baseline: 'AIMetrics';\n      repository: 'github://pharo-ai/metrics';\n      load ]\n```\n\n## How to depend on it\n\n```smalltalk\nspec \n   baseline: 'AIMetrics' \n   with: [ spec repository: 'github://pharo-ai/metrics' ].\n```\n\nTypes of metrics: \n\n- Clustering metrics\n- Regression metrics\n- Classification metrics\n\n### Example: Mean Squared Error (`AIMeanSquaredError`)\n\n```st\n| yTrue yPredicted metric |\nmetric := AIMeanSquaredError new.\nyTrue := #( 3 -0.5 2 7 ).\nyPredicted := #( 2.5 0.0 2 8 ).\nmetric computeForActual: yTrue predicted: yPredicted \"0.375\"\n```\n\n### Example: Accuracy Score (`AIAccuracyScore`)\n\n```st\n| yTrue yPredicted metric |\nmetric := AIAccuracyScore new.\nyTrue := #( 0 1 2 3 ).\nyPredicted := #( 0 2 1 3 ).\nmetric computeForActual: yTrue predicted: yPredicted \"0.5\"\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpharo-ai%2Fmetrics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpharo-ai%2Fmetrics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpharo-ai%2Fmetrics/lists"}