GoogleApi.BigQuery.V2.Model.AggregateClassificationMetrics (google_api_big_query v0.70.1) View Source

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.

Link to this section Summary

Functions

Unwrap a decoded JSON object into its complex fields.

Link to this section Types

Specs

t() :: %GoogleApi.BigQuery.V2.Model.AggregateClassificationMetrics{
  accuracy: float() | nil,
  f1Score: float() | nil,
  logLoss: float() | nil,
  precision: float() | nil,
  recall: float() | nil,
  rocAuc: float() | nil,
  threshold: float() | nil
}

Link to this section Functions

Specs

decode(struct(), keyword()) :: struct()

Unwrap a decoded JSON object into its complex fields.