Isolation Forest¶
Predicts outliers based on minimum path length of random trees with single nodes in the leafs.
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Outputs:
model: model
Model
Configuration:
- n_estimators
- The number of base estimators in the ensemble.
- max_samples
- The number of samples to draw from X to train each base estimator expressed as number of samples (int), or a fraction of all samples (float). If “auto” then a maximum of 256 samples will be used (less when fewer input samples given)
- contamination
- The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function.
- max_features
The number of features to draw from X to train each base estimator.
- If int, then draw max_features features.
- If float, then draw max_features * X.shape features.
- bootstrap
- If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.
- n_jobs
- The number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.
- random_state
- If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.