metrics {nestedcv}

Model performance metrics

Description

Returns model metrics from nestedcv models. Extended metrics including

Usage

metrics(object, extra = FALSE, innerCV = FALSE, positive = 2)

Arguments

object

A 'nestcv.glmnet', 'nestcv.train', 'nestcv.SuperLearner' or 'outercv' object.

extra

Logical whether additional performance metrics are gathered for binary classification models: area under precision recall curve (PR.AUC), Cohen's kappa, F1 score, Matthew's correlation coefficient (MCC).

innerCV

Whether to calculate metrics for inner CV folds. Only available for 'nestcv.glmnet' and 'nestcv.train' objects.

positive

For binary classification, either an integer 1 or 2 for the level of response factor considered to be 'positive' or 'relevant', or a character value for that factor. This affects the F1 score. See caret::confusionMatrix().

Details

Area under precision recall curve is estimated by trapezoidal estimation using MLmetrics::PRAUC().

Value

A named numeric vector of performance metrics.