effector.space_partitioning.Best(min_heterogeneity_decrease_pcg=0.1, heter_small_enough=0.001, max_depth=2, min_samples_leaf=10, numerical_features_grid_size=20, search_partitions_when_categorical=False)

Bases: Base

Choose the algorithm Cart. The algorithm is a greedy algorithm that finds the best split for each level in a greedy fashion.

Parameters:

Name	Type	Description	Default
min_heterogeneity_decrease_pcg	float	Minimum percentage of heterogeneity decrease to accept a split. Example 0.1: if the heterogeneity before any split is 1, the heterogeneity after the first split must be at most 0.9 to be accepted. Otherwise, no split will be accepted.	0.1
heter_small_enough	float	When heterogeneity is smaller than this value, no more splits are performed. Default is 0.001 Value 0.001 is small enough for most cases. It is advisable to set this value to a small number to avoid unnecessary splits. Custom value If you know a priori that a specific heterogeneity value is small enough, you can set this parameter to a higher value than the default.	0.001
max_depth	int	Maximum number of splits to perform Default is 2 2 splits already create 4 subregions, i.e. 4 regional plots per feature, which are already enough. Setting this value to a higher number will increase the number of subregions and plots, which may be too much for the user to analyze.	2
min_samples_leaf	int	Minimum number of instances per subregion Default is 10 If a subregion has less than 10 instances, it may not be representative enough to be analyzed.	10
numerical_features_grid_size	int	Number of candidate split positions for numerical features Default is 20 For numerical features, the algorithm will create a grid of 20 equally spaced values between the minimum and maximum values of the feature.	20
search_partitions_when_categorical	bool	Whether to search for partitions when the feature is categorical refers to a categorical feature of interest This argument asks whether to search for partitions when the feature of interest is categorical. If the feature of interest is numerical, the algorithm will always search for partitions and will consider categorical features for conditioning. Default is False It is difficult to compute the heterogeneity for categorical features, so by default, the algorithm will not search for partitions when the feature of interest is categorical.	False

Source code in effector/space_partitioning.py

def __init__(
        self,
        min_heterogeneity_decrease_pcg: float = 0.1,
        heter_small_enough: float = 0.001,
        max_depth: int = 2,
        min_samples_leaf: int = 10,
        numerical_features_grid_size: int = 20,
        search_partitions_when_categorical: bool = False,
):
    """Choose the algorithm `Cart`.
    The algorithm is a greedy algorithm that finds the best split for each level in a greedy fashion.


    Args:
        min_heterogeneity_decrease_pcg: Minimum percentage of heterogeneity decrease to accept a split.

            ??? Example "Example"
                - `0.1`: if the heterogeneity before any split is 1, the heterogeneity after the first split must be at most 0.9 to be accepted. Otherwise, no split will be accepted.

        heter_small_enough: When heterogeneity is smaller than this value, no more splits are performed.

            ??? Note "Default is `0.001`"
                Value 0.001 is small enough for most cases.
                It is advisable to set this value to a small number to avoid unnecessary splits.

            ??? Note "Custom value"
                If you know a priori that a specific heterogeneity value is small enough,
                you can set this parameter to a higher value than the default.

        max_depth: Maximum number of splits to perform

            ??? Note "Default is `2`"
                2 splits already create 4 subregions, i.e. 4 regional plots per feature, which are already enough.
                Setting this value to a higher number will increase the number of subregions and plots, which may be too much for the user to analyze.

        min_samples_leaf: Minimum number of instances per subregion

            ??? Note "Default is `10`"
                If a subregion has less than 10 instances, it may not be representative enough to be analyzed.

        numerical_features_grid_size: Number of candidate split positions for numerical features

            ??? Note "Default is `20`"
                For numerical features, the algorithm will create a grid of 20 equally spaced values between the minimum and maximum values of the feature.

        search_partitions_when_categorical: Whether to search for partitions when the feature is categorical

            ??? warning "refers to a categorical feature of interest"
                This argument asks whether to search for partitions when the feature of interest is categorical.
                If the feature of interest is numerical, the algorithm will always search for partitions and will consider
                categorical features for conditioning.

            ??? Note "Default is `False`"
                It is difficult to compute the heterogeneity for categorical features, so by default, the algorithm will not search for partitions when the feature of interest is categorical.

    """
    # setters
    self.min_points_per_subregion = min_samples_leaf
    self.nof_candidate_splits_for_numerical = numerical_features_grid_size
    self.max_split_levels = max_depth
    self.heter_pcg_drop_thres = min_heterogeneity_decrease_pcg
    self.heter_small_enough = heter_small_enough
    self.split_categorical_features = search_partitions_when_categorical

    super().__init__("Cart")