pub struct ClassifyEvalReport {Show 28 fields
pub accuracy: f64,
pub avg_loss: f32,
pub per_class_precision: Vec<f64>,
pub per_class_recall: Vec<f64>,
pub per_class_f1: Vec<f64>,
pub per_class_support: Vec<usize>,
pub confusion_matrix: Vec<Vec<usize>>,
pub num_classes: usize,
pub total_samples: usize,
pub eval_time_ms: u64,
pub label_names: Vec<String>,
pub cohens_kappa: f64,
pub mcc: f64,
pub top2_accuracy: f64,
pub mean_confidence: f64,
pub mean_confidence_correct: f64,
pub mean_confidence_wrong: f64,
pub samples_per_sec: f64,
pub calibration_bins: Vec<(f64, f64, usize)>,
pub ece: f64,
pub brier_score: f64,
pub log_loss: f64,
pub ci_accuracy: (f64, f64),
pub ci_macro_f1: (f64, f64),
pub ci_mcc: (f64, f64),
pub baseline_random: f64,
pub baseline_majority: f64,
pub top_confusions: Vec<(usize, usize, usize)>,
}Expand description
Evaluation report from the classification pipeline.
Contains per-class precision/recall/F1, confusion matrix, aggregate metrics,
and advanced diagnostics (Cohen’s kappa, MCC, calibration, confidence distribution).
Produced by [ClassifyTrainer::evaluate] or evaluate_checkpoint.
Fields§
§accuracy: f64Overall accuracy (0.0-1.0)
avg_loss: f32Average cross-entropy loss
per_class_precision: Vec<f64>Per-class precision (0.0-1.0)
per_class_recall: Vec<f64>Per-class recall (0.0-1.0)
per_class_f1: Vec<f64>Per-class F1 score (0.0-1.0)
per_class_support: Vec<usize>Per-class support (sample count)
confusion_matrix: Vec<Vec<usize>>Confusion matrix: confusion_matrix[true][predicted]
num_classes: usizeNumber of classes
total_samples: usizeTotal samples evaluated
eval_time_ms: u64Evaluation wall-clock time in milliseconds
label_names: Vec<String>Human-readable class names
cohens_kappa: f64Cohen’s kappa (chance-corrected agreement, -1 to 1)
mcc: f64Matthews Correlation Coefficient (-1 to 1, robust to class imbalance)
top2_accuracy: f64Top-2 accuracy (correct class in top 2 predictions)
mean_confidence: f64Mean prediction confidence (max softmax probability)
mean_confidence_correct: f64Mean confidence when prediction is correct
mean_confidence_wrong: f64Mean confidence when prediction is wrong
samples_per_sec: f64Samples per second throughput
calibration_bins: Vec<(f64, f64, usize)>Calibration bins: (mean_confidence, mean_accuracy, count) for 10 bins
ece: f64Expected Calibration Error (lower is better, 0 = perfectly calibrated)
brier_score: f64Brier score (multi-class mean squared error of probabilities, lower is better)
log_loss: f64Log loss (negative log-likelihood of true class, lower is better)
ci_accuracy: (f64, f64)Bootstrap 95% confidence intervals: (lower, upper) for (accuracy, macro_f1, mcc)
ci_macro_f1: (f64, f64)§ci_mcc: (f64, f64)§baseline_random: f64Baseline accuracies: (random, majority_class, stratified_random)
baseline_majority: f64§top_confusions: Vec<(usize, usize, usize)>Most confused class pairs: (true_class, pred_class, count)
Implementations§
Source§impl ClassifyEvalReport
impl ClassifyEvalReport
Sourcepub fn to_report(&self) -> String
pub fn to_report(&self) -> String
Format as a human-readable sklearn-style classification report.
Sourcepub fn to_json(&self) -> String
pub fn to_json(&self) -> String
Format as JSON string.
Uses serde_json::json! internally — infallible.
Sourcepub fn to_model_card(
&self,
model_name: &str,
base_model: Option<&str>,
) -> String
pub fn to_model_card( &self, model_name: &str, base_model: Option<&str>, ) -> String
Generate a HuggingFace-compatible model card (README.md) from evaluation results.
Produces a publication-quality model card with YAML front matter, summary metrics, per-class breakdown, confusion matrix (raw + normalized), calibration analysis, intended use, limitations, and ethical considerations.
Trait Implementations§
Source§impl Clone for ClassifyEvalReport
impl Clone for ClassifyEvalReport
Source§fn clone(&self) -> ClassifyEvalReport
fn clone(&self) -> ClassifyEvalReport
Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
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source. Read moreAuto Trait Implementations§
impl Freeze for ClassifyEvalReport
impl RefUnwindSafe for ClassifyEvalReport
impl Send for ClassifyEvalReport
impl Sync for ClassifyEvalReport
impl Unpin for ClassifyEvalReport
impl UnsafeUnpin for ClassifyEvalReport
impl UnwindSafe for ClassifyEvalReport
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