iforest

Isolation Forest (iForest)

Citekey	LiuEtAl2012IsolationBased
Source Code	https://github.com/yzhao062/pyod/blob/master/pyod/models/iforest.py
Learning type	unsupervised
Input dimensionality	multivariate

Parameters

• n_estimators: int, optional (default=100)
  The number of base estimators in the ensemble.

• max_samples: int or float, optional (default="auto")
  The number of samples to draw from X to train each base estimator.

  • If int, then draw max_samples samples.
  • If float, then draw max_samples * X.shape[0] samples.
  • If "auto", then max_samples=min(256, n_samples). If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).

• max_features: int or float, optional (default=1.0)
  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[1] features.

• contamination: float in (0., 0.5), optional (default=0.1)
  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. Automatically determined by algorithm script!!

• bootstrap: bool, optional (default=False)
  If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.

• behaviour: str, default='old'
  Behaviour of the decision_function which can be either 'old' or 'new'. Passing behaviour='new' makes the decision_function change to match other anomaly detection algorithm API which will be the default behaviour in the future. As explained in details in the offset_ attribute documentation, the decision_function becomes dependent on the contamination parameter, in such a way that 0 becomes its natural threshold to detect outliers. REMOVED (old behavior is used per default!).

• random_state: int, RandomState instance or None, optional (default=None)
  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.

• verbose: int, optional (default=0)
  Controls the verbosity of the tree building process.

• n_jobs: integer, optional (default=1)
  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.

Citation format (for source code)

Zhao, Y., Nasrullah, Z. and Li, Z., 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. Journal of machine learning research (JMLR), 20(96), pp.1-7.