BalancedAccuracy
Average of recall obtained on each class.
Balanced Accuracy is the macro-average of recall scores per class. It avoids the inflated performance estimates that plain accuracy gives on imbalanced datasets, since each class contributes equally regardless of how many samples it has.
::
Balanced Accuracy = (1 / C) * sum(recall_c for c in classes)
Range: [0, 1], higher is better (MAXIMIZE = True).
References
Methods
score(true_labels: 'DashAIDataset', probs_pred_labels: 'np.ndarray') -> float
BalancedAccuracyCalculate the balanced accuracy between true and predicted labels.
Parameters
- true_labels : DashAIDataset
- A DashAI dataset with labels.
- probs_pred_labels : np.ndarray
- A two-dimensional matrix in which each column represents a class and the row values represent the probability that an example belongs to the class associated with the column.
Returns
- float
- Balanced accuracy score between true labels and predicted labels
get_metadata(cls: 'BaseMetric') -> Dict[str, Any]
BaseMetricGet metadata values for the current metric.
Returns
- Dict[str, Any]
- Dictionary with the metadata
is_multiclass(true_labels: 'np.ndarray') -> bool
ClassificationMetricDetermine if the classification problem is multiclass (more than 2 classes).
Parameters
- true_labels : np.ndarray
- Array of true labels.
Returns
- bool
- True if the problem has more than 2 unique classes, False otherwise.