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 is such that is equal to the number of observations known to be in group and predicted to be in group .
Thus in binary classification, the count of true negatives is , false negatives is , true positives is and false positives is .
- Args:
- df: snowpark.DataFrame
Input dataframe.
- y_true_col_name: string or list of strings
Column name representing actual values.
- y_pred_col_name: string or list of strings
Column name representing predicted values.
- labels: list of labels, default=None
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: string, default=None
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}.