Class Mapping Aggregation Settings

For multiclass projects with a lot of unique values in target column you can specify the parameters for aggregation of rare values to improve the modeling performance and decrease the runtime and resource usage of resulting models.

class datarobot.helpers.ClassMappingAggregationSettings(max_unaggregated_class_values=None, min_class_support=None, excluded_from_aggregation=None, aggregation_class_name=None)

Class mapping aggregation settings. For multiclass projects allows fine control over which target values will be preserved as classes. Classes which aren’t preserved will be - aggregated into a single “catch everything else” class in case of multiclass - or will be ignored in case of multilabel. All attributes are optional, if not specified - server side defaults will be used.

Attributes:

max_unaggregated_class_valuesint, optional

Maximum amount of unique values allowed before aggregation kicks in.

min_class_supportint, optional

Minimum number of instances necessary for each target value in the dataset. All values with less instances will be aggregated.

excluded_from_aggregationlist, optional

List of target values that should be guaranteed to kept as is, regardless of other settings.

aggregation_class_namestr, optional

If some of the values will be aggregated - this is the name of the aggregation class that will replace them.