Struct AggregateClassificationMetrics

pub struct AggregateClassificationMetrics {
    pub accuracy: Option<f64>,
    pub f1_score: Option<f64>,
    pub log_loss: Option<f64>,
    pub precision: Option<f64>,
    pub recall: Option<f64>,
    pub roc_auc: Option<f64>,
    pub threshold: Option<f64>,
}

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.

This type is not used in any activity, and only used as part of another schema.

Fields

accuracy: Option<f64>

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

f1_score: Option<f64>

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

log_loss: Option<f64>

Logarithmic Loss. 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.

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.

roc_auc: Option<f64>

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

Trait Implementations

impl Clone for AggregateClassificationMetrics

fn clone(&self) -> AggregateClassificationMetrics

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AggregateClassificationMetrics

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for AggregateClassificationMetrics

fn default() -> AggregateClassificationMetrics

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for AggregateClassificationMetrics

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for AggregateClassificationMetrics

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for AggregateClassificationMetrics