IF¶
- class frlearn.data_descriptors.IF(psi: int | Callable[[int], int] = 256, t: int = 100, random_state: int = 0, preprocessors=(), **sklearn_params)[source]¶
Wrapper for the Isolation Forest (IF) data descriptor [1] implemented in scikit-learn. Expresses the effort required to isolate a query instance from the target data by random splits on attribute values.
- Parameters
- psiint or (int -> int) = 256
Sub-sampling size. Number of training instances to use for each random tree. Should be either a positive integer, or a function that takes the size of the target class and returns such an integer. If the size of the target class is a smaller number, that will be used instead.
- tint = 100
Number of random trees.
- random_stateint = 0
Random state to use.
- preprocessorsiterable = ()
Preprocessors to apply.
- sklearn_params
Additional keyword parameters will be passed on as-is to scikit-learn’s IsolationForest constructor.
Notes
Scores are the complement of the anomaly scores in [1]. psi and t are two hyperparameters that can potentially be tuned, but the default values should be good enough [1].
References
