`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¶

`auc` |
Compute the area under the ROC curve for the given targets and predictions. |

`accuracy` |
Compute the accuracy score; which measures the fraction of predictions made by the classifier that are exactly correct. |

`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. |