Struct ClassificationMetrics 

pub struct ClassificationMetrics {
    pub loss: f64,
    pub accuracy: f64,
    pub precision: f64,
    pub recall: f64,
    pub f1_score: f64,
}

Performance metrics for classification models.

This struct contains common evaluation metrics used to assess the performance of classification models, including accuracy, precision, recall, and F1 score.

Fields

loss: f64

The loss value (e.g., cross-entropy loss) from the model’s predictions.

accuracy: f64

The accuracy of the model, measured as the proportion of correctly classified instances out of the total instances. Range: [0.0, 1.0], where 1.0 means perfect classification.

precision: f64

The precision of the model, measured as the ratio of true positives to the sum of true positives and false positives. Range: [0.0, 1.0], where 1.0 means no false positives.

recall: f64

The recall (sensitivity) of the model, measured as the ratio of true positives to the sum of true positives and false negatives. Range: [0.0, 1.0], where 1.0 means no false negatives.

f1_score: f64

The F1 score, which is the harmonic mean of precision and recall. Range: [0.0, 1.0], where 1.0 means perfect precision and recall. F1 = 2 * (precision * recall) / (precision + recall)

Trait Implementations

impl Debug for ClassificationMetrics

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

Formats the value using the given formatter.