# evaluation¶

The evaluation module includes performance metrics to evaluate machine learning models. The metrics can be broadly categorized as: - Classification metrics - Regression metrics

The evaluation module supports the following classification metrics: - accuracy - auc - confusion_matrix - f1_score - fbeta_score - log_loss - precision - recall - roc_curve

The evaluation module supports the following regression metrics: - rmse - max_error

## classifier metrics¶

 accuracy Compute the accuracy score; which measures the fraction of predictions made by the classifier that are exactly correct. auc Compute the area under the ROC curve for the given targets and predictions. confusion_matrix Compute the confusion matrix for classifier predictions. f1_score Compute the F1 score (sometimes known as the balanced F-score or F-measure). fbeta_score Compute the F-beta score. log_loss Compute the logloss for the given targets and the given predicted probabilities. precision Compute the precision score for classification tasks. recall Compute the recall score for classification tasks. roc_curve Compute an ROC curve for the given targets and predictions.

## regression metrics¶

 max_error Compute the maximum absolute deviation between two SArrays. rmse Compute the root mean squared error between two SArrays.