Compute accuracy, binary crossentropy and (optionally) AUC or AUPRC, given predictions and
true targets. Outputs columnwise average.
Usage
evaluate_sigmoid(y, y_conf, auc = FALSE, auprc = FALSE, label_names = NULL)
Arguments
- y
Matrix of true target.
- y_conf
Matrix of predictions.
- auc
Whether to include AUC metric.
- auprc
Whether to include AUPRC metric.
- label_names
Names of corresponding labels. Length must be equal to number of columns of y.
Value
A list of evaluation results.
Examples
if (FALSE) { # reticulate::py_module_available("tensorflow")
y <- matrix(sample(c(0, 1), 30, replace = TRUE), ncol = 3)
y_conf <- matrix(runif(n = 30), ncol = 3)
evaluate_sigmoid(y, y_conf, auc = TRUE, auprc = TRUE)
}
