Isolation Forest

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

  Changed in version 0.20: The default value of contamination will change from 0.1 in 0.20 to 'auto' in 0.22.

• 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

  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

Input ports:

Output ports:

model : model

Model

n_estimators (n_estimators)

The number of base estimators in the ensemble.

max_samples (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 (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. If ‘auto’, the decision function threshold is determined as in the original paper.

Changed in version 0.20: The default value of contamination will change from 0.1 in 0.20 to 'auto' in 0.22.

max_features (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 (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 (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 (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.

class node_isolationforest.IsolationForest