rust_ml/bench/
classification_metrics.rs

1/// Performance metrics for classification models.
2///
3/// This struct contains common evaluation metrics used to assess the performance
4/// of classification models, including accuracy, precision, recall, and F1 score.
5#[derive(Debug)]
6pub struct ClassificationMetrics {
7    /// The loss value (e.g., cross-entropy loss) from the model's predictions.
8    pub loss: f64,
9
10    /// The accuracy of the model, measured as the proportion of correctly
11    /// classified instances out of the total instances.
12    /// Range: [0.0, 1.0], where 1.0 means perfect classification.
13    pub accuracy: f64,
14
15    /// The precision of the model, measured as the ratio of true positives to
16    /// the sum of true positives and false positives.
17    /// Range: [0.0, 1.0], where 1.0 means no false positives.
18    pub precision: f64,
19
20    /// The recall (sensitivity) of the model, measured as the ratio of true positives
21    /// to the sum of true positives and false negatives.
22    /// Range: [0.0, 1.0], where 1.0 means no false negatives.
23    pub recall: f64,
24
25    /// The F1 score, which is the harmonic mean of precision and recall.
26    /// Range: [0.0, 1.0], where 1.0 means perfect precision and recall.
27    /// F1 = 2 * (precision * recall) / (precision + recall)
28    pub f1_score: f64,
29}
rust_ml/bench/classification_metrics.rs

rust_ml/bench/
classification_metrics.rs
