ML.EVAL.CLASSIFICATION.LOG_LOSS

Returns the log loss.

Syntax

ML.EVAL.CLASSIFICATION.LOG_LOSS(y_true, y_score, normalize, sample_weight, labels)

Arguments

Name	Type	Default	Description
y_true	object		DataFrame or array object of ground-truth target values.
y_score	object		DataFrame or array object of predicted probabilities or decision scores (e.g. output of ML.PREDICT_PROBA or ML.PREDICT).
normalize	Any	TRUE	If TRUE, return the per-sample average loss. If FALSE, return the summed loss across samples.
sample_weight	Any	None	Optional DataFrame or array object of per-sample weights. Omit for uniform weights.
labels	object	None	Optional array of class labels to include in the score, in the order they should appear.

Examples

Examples coming soon

Working Excel formula examples for this function are not yet written.

See also

• ML.EVAL.CLASSIFICATION.ACCURACY
• ML.EVAL.CLASSIFICATION.AVERAGE_PRECISION
• ML.EVAL.CLASSIFICATION.BALANCED_ACCURACY
• ML.EVAL.CLASSIFICATION.BRIER_SCORE_LOSS
• ML.EVAL.CLASSIFICATION.D2_LOG_LOSS_SCORE
• ML.EVAL.CLASSIFICATION.F1
• ML.EVAL.CLASSIFICATION.JACCARD
• ML.EVAL.CLASSIFICATION.MATTHEWS_CORRCOEF
• ML.EVAL.CLASSIFICATION.PRECISION
• ML.EVAL.CLASSIFICATION.RECALL
• ML.EVAL.CLASSIFICATION.ROC_AUC
• ML.EVAL.CLASSIFICATION.TOP_K_ACCURACY