gcp_bigquery_client/model/
aggregate_classification_metrics.rs

1//! 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.
2
3#[derive(Debug, Default, Clone, Serialize, Deserialize)]
4#[serde(rename_all = "camelCase")]
5pub struct AggregateClassificationMetrics {
6    /// Area Under a ROC Curve. For multiclass this is a macro-averaged metric.
7    pub roc_auc: Option<f64>,
8    /// 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.
9    pub precision: Option<f64>,
10    /// The F1 score is an average of recall and precision. For multiclass this is a macro-averaged metric.
11    pub f_1_score: Option<f64>,
12    /// Accuracy is the fraction of predictions given the correct label. For multiclass this is a micro-averaged metric.
13    pub accuracy: Option<f64>,
14    /// 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.
15    pub threshold: Option<f64>,
16    /// Logarithmic Loss. For multiclass this is a macro-averaged metric.
17    pub log_loss: Option<f64>,
18    /// Recall is the fraction of actual positive labels that were given a positive prediction. For multiclass this is a macro-averaged metric.
19    pub recall: Option<f64>,
20}
gcp_bigquery_client/model/aggregate_classification_metrics.rs

gcp_bigquery_client/model/
aggregate_classification_metrics.rs
