metrics

// jda::ml::metrics — Model Evaluation Metrics

Functions

Function	Description
`true_positives`
`true_negatives`
`false_positives`
`false_negatives`
`accuracy`
`precision`
`recall`
`f1_score`
`f_beta`
`specificity`
`matthews_corrcoef`
`confusion_matrix`
`cm_precision`
`cm_recall`
`f1_macro`
`f1_weighted`
`f1_micro`
`roc_auc`
`average_precision`
`mae`
`mse`
`rmse`
`r_squared`
`mape`
`huber_loss`
`median_absolute_error`
`hamming_loss`
`subset_accuracy`
`print_classification_report`
`print_confusion_matrix`

fn true_positives(pred: ref []i64, true: ref []i64) -> i64

fn true_negatives(pred: ref []i64, true: ref []i64) -> i64

fn false_positives(pred: ref []i64, true: ref []i64) -> i64

fn false_negatives(pred: ref []i64, true: ref []i64) -> i64

fn accuracy(pred: ref []i64, true: ref []i64) -> f64

fn precision(pred: ref []i64, true: ref []i64) -> f64

fn recall(pred: ref []i64, true: ref []i64) -> f64

fn f1_score(pred: ref []i64, true: ref []i64) -> f64

fn f_beta(pred: ref []i64, true: ref []i64, beta: f64) -> f64

fn specificity(pred: ref []i64, true: ref []i64) -> f64

fn matthews_corrcoef(pred: ref []i64, true: ref []i64) -> f64

fn confusion_matrix(pred: ref []i64, true: ref []i64,

fn cm_precision(cm: ref []i64, c: i64, n_classes: i64) -> f64

fn cm_recall(cm: ref []i64, c: i64, n_classes: i64) -> f64

fn f1_macro(pred: ref []i64, true: ref []i64, n_classes: i64) -> f64

fn f1_weighted(pred: ref []i64, true: ref []i64, n_classes: i64) -> f64

fn f1_micro(pred: ref []i64, true: ref []i64, n_classes: i64) -> f64

fn roc_auc(scores: ref []f32, true: ref []i64) -> f64

fn average_precision(scores: ref []f32, true: ref []i64) -> f64

fn mae(pred: ref []f32, true: ref []f32) -> f64

fn mse(pred: ref []f32, true: ref []f32) -> f64

fn rmse(pred: ref []f32, true: ref []f32) -> f64

fn r_squared(pred: ref []f32, true: ref []f32) -> f64

fn mape(pred: ref []f32, true: ref []f32) -> f64

fn huber_loss(pred: ref []f32, true: ref []f32, delta: f32) -> f64

fn median_absolute_error(pred: ref []f32, true: ref []f32) -> f64

fn hamming_loss(pred: ref [][]i64, true: ref [][]i64) -> f64

fn subset_accuracy(pred: ref [][]i64, true: ref [][]i64) -> f64

fn print_classification_report(pred: ref []i64, true: ref []i64,

fn print_confusion_matrix(cm: ref []i64, n_classes: i64,