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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")) 
}