autointent.metrics.decision_accuracy#
- autointent.metrics.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.custom_types.ListOfGenericLabels) – True values of labels
y_pred (autointent.custom_types.ListOfGenericLabels) – Predicted values of labels
- Returns:
Score of the decision accuracy
- Return type: