autointent.metrics.decision.decision_accuracy#
- autointent.metrics.decision.decision_accuracy(y_true, y_pred)#
Calculate decision accuracy. Supports both multiclass and multilabel.
The decision accuracy is calculated as:
\[\text{Accuracy} = \frac{\sum_{i=1}^N \mathbb{1}(y_{\text{true},i} = y_{\text{pred},i})}{N}\]where: - \(N\) is the total number of samples, - \(y_{\text{true},i}\) is the true label for the \(i\)-th sample, - \(y_{\text{pred},i}\) is the predicted label for the \(i\)-th sample, - \(\mathbb{1}(\text{condition})\) is the indicator function that equals 1 if the condition is true and 0 otherwise.
- Parameters:
y_true (autointent.metrics.custom_types.LABELS_VALUE_TYPE) – True values of labels
y_pred (autointent.metrics.custom_types.LABELS_VALUE_TYPE) – Predicted values of labels
- Returns:
Score of the decision accuracy
- Return type: