evalml.data_checks.ClassImbalanceDataCheck.__init__

ClassImbalanceDataCheck.__init__(threshold=0.1, num_cv_folds=3)

Check if any of the target labels are imbalanced, or if the number of values for each target

are below 2 times the number of cv folds

Parameters

• threshold (float) – The minimum threshold allowed for class imbalance before a warning is raised. A perfectly balanced dataset would have a threshold of (1/n_classes), ie 0.50 for binary classes. Defaults to 0.10

• num_cv_folds (int) – The number of cross-validation folds. Must be positive. Choose 0 to ignore this warning.

evalml.data_checks.ClassImbalanceDataCheck evalml.data_checks.ClassImbalanceDataCheck.validate