Metrics¶
The metric of a usecase is the function that will be used to assess for the efficiency of its models. The metrics you can choose depends on the type of usecase you are training.
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previsionio.metrics.
Classification
¶ Metrics for classification projects Available metrics in prevision:
auc, log_loss, error_rate_binary-
AUC
= 'auc'¶ Area Under ROC Curve
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error_rate
= 'error_rate_binary'¶ Error rate
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log_loss
= 'log_loss'¶ Logarithmic Loss
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previsionio.metrics.
Clustering
¶ Metrics for clustering projects
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calinski_harabaz
= 'calinski_harabaz'¶ Clustering calinski_harabaz metric
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silhouette
= 'silhouette'¶ Clustering silhouette metric
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previsionio.metrics.
MultiClassification
¶ Metrics for multiclassification projects
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error_rate
= 'error_rate_multi'¶ Multi-class Error rate
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log_loss
= 'log_loss'¶ Logarithmic Loss
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previsionio.metrics.
Regression
¶ Metrics for regression projects Available metrics in prevision:
rmse, mape, rmsle, mse, mae-
MAE
= 'mae'¶ Mean Average Error
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MAPE
= 'mape'¶ Mean Average Percentage Error
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MSE
= 'mse'¶ Mean squared Error
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RMSE
= 'rmse'¶ Root Mean Squared Error
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RMSLE
= 'rmsle'¶ Root Mean Squared Logarithmic Error
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