BinaryAccuracy |
Compute binary accuracy score, which is the frequency of input matching target. |
BinaryAUPRC |
Compute AUPRC, also called Average Precision, which is the area under the Precision-Recall Curve, for binary classification. |
BinaryAUROC |
Compute AUROC, which is the area under the ROC Curve, for binary classification. |
BinaryBinnedAUROC |
Compute AUROC, which is the area under the ROC Curve, for binary classification. |
BinaryBinnedAUPRC |
Compute Binned AUPRC, which is the area under the binned version of the Precision Recall Curve, for binary classification. |
BinaryBinnedPrecisionRecallCurve |
Compute precision recall curve with given thresholds. |
BinaryConfusionMatrix |
Compute binary confusion matrix, a 2 by 2 tensor with counts ( (true positive, false negative) , (false positive, true negative) ) See also MulticlassConfusionMatrix |
BinaryF1Score |
Compute binary f1 score, which is defined as the harmonic mean of precision and recall. |
BinaryNormalizedEntropy |
Compute the normalized binary cross entropy between predicted input and ground-truth binary target. |
BinaryPrecision |
Compute the precision score for binary classification tasks, which is calculated as the ratio of the true positives and the sum of true positives and false positives. |
BinaryPrecisionRecallCurve |
Returns precision-recall pairs and their corresponding thresholds for binary classification tasks. |
BinaryRecall |
Compute the recall score for binary classification tasks, which is calculated as the ratio of the true positives and the sum of true positives and false negatives. |
BinaryRecallAtFixedPrecision |
Returns the highest possible recall value give the minimum precision for binary classification tasks. |
MulticlassAccuracy |
Compute accuracy score, which is the frequency of input matching target. |
MulticlassAUPRC |
Compute AUPRC, also called Average Precision, which is the area under the Precision-Recall Curve, for multiclass classification. |
MulticlassAUROC |
Compute AUROC, which is the area under the ROC Curve, for multiclass classification in a one vs rest fashion. |
MulticlassBinnedAUPRC |
Compute Binned AUPRC, which is the area under the binned version of the Precision Recall Curve, for multiclass classification. |
MulticlassBinnedAUROC |
Compute AUROC, which is the area under the ROC Curve, for multiclass classification. |
MulticlassBinnedPrecisionRecallCurve |
Compute precision recall curve with given thresholds. |
MulticlassConfusionMatrix |
Compute multi-class confusion matrix, a matrix of dimension num_classes x num_classes where each element at position (i,j) is the number of examples with true class i that were predicted to be class j. |
MulticlassF1Score |
Compute f1 score, which is defined as the harmonic mean of precision and recall. |
MulticlassPrecision |
Compute the precision score, the ratio of the true positives and the sum of true positives and false positives. |
MulticlassPrecisionRecallCurve |
Returns precision-recall pairs and their corresponding thresholds for multi-class classification tasks. |
MulticlassRecall |
Compute the recall score, the ratio of the true positives and the sum of true positives and false negatives. |
MultilabelAccuracy |
Compute multilabel accuracy score, which is the frequency of input matching target. |
MultilabelAUPRC |
Compute AUPRC, also called Average Precision, which is the area under the Precision-Recall Curve, for multilabel classification. |
MultilabelBinnedAUPRC |
Compute Binned AUPRC, which is the area under the binned version of the Precision Recall Curve, for multilabel classification. |
MultilabelBinnedPrecisionRecallCurve |
Compute precision recall curve with given thresholds. |
MultilabelPrecisionRecallCurve |
Returns precision-recall pairs and their corresponding thresholds for multi-label classification tasks. |
MultilabelRecallAtFixedPrecision |
Returns the highest possible recall value given the minimum precision for each label and their corresponding thresholds for multi-label classification tasks. |
TopKMultilabelAccuracy |
Compute multilabel accuracy score, which is the frequency of the top k label predicted matching target. |