Struct tangram_metrics::ClassMetrics

pub struct ClassMetrics {
    pub true_positives: u64,
    pub false_positives: u64,
    pub true_negatives: u64,
    pub false_negatives: u64,
    pub accuracy: f32,
    pub precision: f32,
    pub recall: f32,
    pub f1_score: f32,
}

ClassMetrics are class specific metrics used to evaluate the model’s performance on each individual class.

Fields

true_positives: u64

This is the total number of examples whose label is equal to this class that the model predicted as belonging to this class.

false_positives: u64

This is the total number of examples whose label is not equal to this class that the model predicted as belonging to this class.

true_negatives: u64

This is the total number of examples whose label is not equal to this class that the model predicted as not belonging to this class.

false_negatives: u64

This is the total number of examples whose label is equal to this class that the model predicted as not belonging to this class.

accuracy: f32

The accuracy is the fraction of examples of this class that were correctly classified.

precision: f32

The precision is the fraction of examples the model predicted as belonging to this class whose label is actually equal to this class. precision = true_positives / (true_positives + false_positives). See Precision and Recall.

recall: f32

The recall is the fraction of examples in the dataset whose label is equal to this class that the model predicted as equal to this class. recall = true_positives / (true_positives + false_negatives).

f1_score: f32

The f1 score is the harmonic mean of the precision and the recall. See F1 Score.

Trait Implementations

impl Debug for ClassMetrics

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

Formats the value using the given formatter.