snowflake.ml.modeling.metrics.confusion_matrix¶

snowflake.ml.modeling.metrics.confusion_matrix(*, df: DataFrame, y_true_col_name: str, y_pred_col_name: str, labels: Optional[Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None, sample_weight_col_name: Optional[str] = None, normalize: Optional[str] = None) → Union[ndarray[Any, dtype[int64]], ndarray[Any, dtype[float64]]]¶

Compute confusion matrix to evaluate the accuracy of a classification.

By definition a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to the number of observations known to be in group \(i\) and predicted to be in group \(j\).

Thus in binary classification, the count of true negatives is \(C_{0,0}\), false negatives is \(C_{1,0}\), true positives is \(C_{1,1}\) and false positives is \(C_{0,1}\).

Args:

df: Input dataframe. y_true_col_name: Column name representing actual values. y_pred_col_name: Column name representing predicted values. labels: List of labels to index the matrix. This may be used to

reorder or select a subset of labels. If None is given, those that appear at least once in the y true or y pred column are used in sorted order.

sample_weight_col_name: Column name representing sample weights. normalize: {‘true’, ‘pred’, ‘all’}, default=None

Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.

Returns:

C: ndarray of shape (n_classes, n_classes)

Confusion matrix whose i-th row and j-th column entry indicates the number of samples with true label being i-th class and predicted label being j-th class.

Raises:

ValueError: The given labels is empty. ValueError: No label specified in the given labels is in the y true column. ValueError: normalize is not one of {‘true’, ‘pred’, ‘all’, None}.