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HammingDistance

Metric
DashAI.back.metrics.classification.HammingDistance

Fraction of labels that are incorrectly predicted (Hamming loss).

The Hamming distance (or Hamming loss) is the fraction of the total predictions that are wrong. For single-label classification it equals 1 - accuracy; for multi-label classification it counts per-label mismatches independently, making it especially useful when each sample can belong to multiple classes simultaneously.

A lower Hamming distance indicates better performance. MAXIMIZE = False so the DashAI framework treats smaller values as improvements.

::

Hamming Loss = (1/N) · Σᵢ 𝟙[ŷᵢ ≠ yᵢ]

Range: [0, 1], lower is better (MAXIMIZE = False).

References​

Methods​

score(true_labels: 'DashAIDataset', probs_pred_labels: 'np.ndarray', multiclass: Optional[bool] = None) -> float

Defined on HammingDistance

Calculate Hamming Distance between true labels 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.
multiclass : bool, optional
Whether the task is a multiclass classification. If None, it will be determined automatically from the number of unique labels.

Returns

float
Hamming Distance between true labels and predicted labels

get_metadata(cls: 'BaseMetric') -> Dict[str, Any]

Defined on BaseMetric

Get metadata values for the current metric.

Returns

Dict[str, Any]
Dictionary with the metadata

is_multiclass(true_labels: 'np.ndarray') -> bool

Defined on ClassificationMetric

Determine 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.

Compatible with​