ARFFIMTDD#
- class capymoa.regressor.ARFFIMTDD[source]#
Bases:
MOARegressorModified Fast Incremental Model Tree with Drift Detection for basic learner for ARF-Regas described by Ikonomovska et al.
- __init__(
- schema: Schema,
- subspace_size_size: int = 2,
- split_criterion: SplitCriterion | str = 'VarianceReductionSplitCriterion',
- grace_period: int = 200,
- split_confidence: float = 1e-07,
- tie_threshold: float = 0.05,
- page_hinckley_alpha: float = 0.005,
- page_hinckley_threshold: int = 50,
- alternate_tree_fading_factor: float = 0.995,
- alternate_tree_t_min: int = 150,
- alternate_tree_time: int = 1500,
- learning_ratio: float = 0.02,
- learning_ratio_decay_factor: float = 0.001,
- learning_ratio_const: bool = False,
- random_seed: int | None = None,
Construct ARFFIMTDD.
- Parameters:
subspace_size_size – Number of features per subset for each node split. Negative values = #features - k
split_criterion – Split criterion to use.
grace_period – Number of instances a leaf should observe between split attempts.
split_confidence – Allowed error in split decision, values close to 0 will take long to decide.
tie_threshold – Threshold below which a split will be forced to break ties.
page_hinckley_alpha – Alpha value to use in the Page Hinckley change detection tests.
page_hinckley_threshold – Threshold value used in the Page Hinckley change detection tests.
alternate_tree_fading_factor – Fading factor used to decide if an alternate tree should replace an original.
alternate_tree_t_min – Tmin value used to decide if an alternate tree should replace an original.
alternate_tree_time – The number of instances used to decide if an alternate tree should be discarded.
learning_ratio – Learning ratio to used for training the Perceptrons in the leaves.
learning_ratio_decay_factor – Learning rate decay factor (not used when learning rate is constant).
learning_ratio_const – Keep learning rate constant instead of decaying.