View Source GoogleApi.BigQuery.V2.Model.AggregateClassificationMetrics (google_api_big_query v0.78.0)
Aggregate metrics for classification/classifier models. For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.
Attributes
-
accuracy
(type:float()
, default:nil
) - Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric. -
f1Score
(type:float()
, default:nil
) - The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric. -
logLoss
(type:float()
, default:nil
) - Logarithmic Loss. For multiclass this is a macro-averaged metric. -
precision
(type:float()
, default:nil
) - Precision is the fraction of actual positive predictions that had positive actual labels. For multiclass this is a macro-averaged metric treating each class as a binary classifier. -
recall
(type:float()
, default:nil
) - Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric. -
rocAuc
(type:float()
, default:nil
) - Area Under a ROC Curve. For multiclass this is a macro-averaged metric. -
threshold
(type:float()
, default:nil
) - Threshold at which the metrics are computed. For binary classification models this is the positive class threshold. For multi-class classfication models this is the confidence threshold.
Summary
Functions
Unwrap a decoded JSON object into its complex fields.
Types
Functions
Unwrap a decoded JSON object into its complex fields.