pub struct AggregateClassificationMetrics {
    pub roc_auc: Option<f64>,
    pub precision: Option<f64>,
    pub f_1_score: Option<f64>,
    pub accuracy: Option<f64>,
    pub threshold: Option<f64>,
    pub log_loss: Option<f64>,
    pub recall: Option<f64>,
}

Fields

roc_auc: Option<f64>

Area Under a ROC Curve. For multiclass this is a macro-averaged metric.

precision: Option<f64>

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.

f_1_score: Option<f64>

The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.

accuracy: Option<f64>

Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.

threshold: Option<f64>

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.

log_loss: Option<f64>

Logarithmic Loss. For multiclass this is a macro-averaged metric.

recall: Option<f64>

Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.

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