google_api_big_query v0.6.0 GoogleApi.BigQuery.V2.Model.AggregateClassificationMetrics View Source
Aggregate metrics for classification models. For multi-class models, the metrics are either macro-averaged: metrics are calculated for each label and then an unweighted average is taken of those values or micro-averaged: the metric is calculated globally by counting the total number of correctly predicted rows.
Attributes
- accuracy (float()): Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric. Defaults to:
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. - f1Score (float()): The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric. Defaults to:
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. - logLoss (float()): Logarithmic Loss. For multiclass this is a macro-averaged metric. Defaults to:
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. - precision (float()): 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. Defaults to:
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. - recall (float()): Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric. Defaults to:
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. - rocAuc (float()): Area Under a ROC Curve. For multiclass this is a macro-averaged metric. Defaults to:
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. - threshold (float()): 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. Defaults to:
null
.
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Functions
Unwrap a decoded JSON object into its complex fields.
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decode(value, options) View Source
Unwrap a decoded JSON object into its complex fields.