SplitCandidate¶
- class SplitCandidate(feature_idx, threshold, gain, left_indices, right_indices, is_oblique=False, oblique_weights=None, validation_score=None, variance_estimate=None, consensus_support=None)[source]¶
Bases:
objectRepresents a potential split with all relevant information.
- feature_idx¶
Index of the feature to split on.
- Type:
int
- threshold¶
Threshold value for the split.
- Type:
float
- gain¶
Information gain or improvement from this split.
- Type:
float
- left_indices¶
Indices of samples going to left child.
- Type:
numpy.ndarray[tuple[Any, …], numpy.dtype[numpy.int64]]
- right_indices¶
Indices of samples going to right child.
- Type:
numpy.ndarray[tuple[Any, …], numpy.dtype[numpy.int64]]
- is_oblique¶
Whether this is an oblique (linear combination) split.
- Type:
bool
- oblique_weights¶
Weights for oblique split, None for axis-aligned splits.
- Type:
numpy.ndarray[tuple[Any, …], numpy.dtype[numpy.floating]] | None
- validation_score¶
Validation score for this split.
- Type:
float | None
- variance_estimate¶
Estimated variance for this split.
- Type:
float | None
- consensus_support¶
Consensus support score from bootstrap sampling.
- Type:
float | None
- __init__(feature_idx, threshold, gain, left_indices, right_indices, is_oblique=False, oblique_weights=None, validation_score=None, variance_estimate=None, consensus_support=None)¶
Methods
__init__(feature_idx, threshold, gain, ...)Attributes
- feature_idx: int¶
- threshold: float¶
- gain: float¶
- left_indices: ndarray[tuple[Any, ...], dtype[int64]]¶
- right_indices: ndarray[tuple[Any, ...], dtype[int64]]¶
- is_oblique: bool¶
- oblique_weights: ndarray[tuple[Any, ...], dtype[floating]] | None¶
- validation_score: float | None¶
- variance_estimate: float | None¶
- consensus_support: float | None¶
- __init__(feature_idx, threshold, gain, left_indices, right_indices, is_oblique=False, oblique_weights=None, validation_score=None, variance_estimate=None, consensus_support=None)¶