Compute confusion matrix, accuracy, categorical crossentropy and (optionally) AUC or AUPRC, given predictions and
true targets. AUC and AUPRC only possible for 2 targets.
Usage
evaluate_softmax(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. Only possible for 2 targets.
- auprc
Whether to include AUPRC metric. Only possible for 2 targets.
- 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(c(1, 0, 0, 0, 1, 1), ncol = 2)
y_conf <- matrix(c(0.3, 0.5, 0.1, 0.7, 0.5, 0.9), ncol = 2)
evaluate_softmax(y, y_conf, auc = TRUE, auprc = TRUE, label_names = c("A", "B"))
}
