AccuracyScore reports (weighted) true values/nSamples.

AUC Compute Area Under the Curve (AUC) using the trapezoidal rule.

AveragePrecisionScore compute average precision (AP) from prediction scores.

ConfusionMatrix Compute confusion matrix to evaluate the accuracy of a classification operate only on 1st Y column uses preprocessing.LabelEncoder to map class values to class indices.

F1Score v https://en.wikipedia.org/wiki/F1_score average must be macro|micro|weighted.

FBetaScore is the weighted harmonic mean of precision and recall, reaching its optimal value at 1 and its worst value at 0.

MeanAbsoluteError regression loss Read more in the :ref:`User Guide <mean_absolute_error>`.

MeanSquaredError regression loss Read more in the :ref:`User Guide <mean_squared_error>`.

PrecisionRecallCurve compute precision-recall pairs for different probability thresholds Note: this implementation is restricted to the binary classification task.

PrecisionRecallFScoreSupport Compute precision, recall, F-measure and support for each class operate only on 1st Y column average must be macro|micro|weighted.

PrecisionScore v https://en.wikipedia.org/wiki/F1_score average must be macro|micro|weighted.

R2Score """R^2 (coefficient of determination) regression score function.

RecallScore v https://en.wikipedia.org/wiki/F1_score average must be macro|micro|weighted.

ROCAUCScore compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.

ROCCurve Compute Receiver operating characteristic (ROC) y_true : array, shape = [n_samples] True binary labels in range {0, 1} or {-1, 1}.