Isolation Forest

Predicts outliers based on minimum path length of random trees with single nodes in the leafs.

Documentation

Predicts outliers based on minimum path length of random trees with single nodes in the leafs.

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 scores of the samples.

  • If ‘auto’, the threshold is determined as in the original paper.

  • If float, the contamination should be in the range [0, 0.5].

  Changed in version 0.22: The default value of contamination changed from 0.1 to 'auto'.

• 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(). None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details.

• random_state

  Controls the pseudo-randomness of the selection of the feature and split values for each branching step and each tree in the forest.

  Pass an int for reproducible results across multiple function calls. See random_state.

Attributes:

• estimators_samples_

  The subset of drawn samples (i.e., the in-bag samples) for each base estimator.

• max_samples_

  The actual number of samples.

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_isolationforest.IsolationForest