Isolation Forest

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_isolationforest.IsolationForest

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 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.

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

Inputs: Outputs:

model : model

Model