class sklearn.ensemble.IsolationForest(n_estimators=100, max_samples=’auto’, contamination=’legacy’, max_features=1.0, bootstrap=False, n_jobs=None, behaviour=’old’, random_state=None, verbose=0)
[source]
Isolation Forest Algorithm
Return the anomaly score of each sample using the IsolationForest algorithm
The IsolationForest ‘isolates’ observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.
Since recursive partitioning can be represented by a tree structure, the number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node.
This path length, averaged over a forest of such random trees, is a measure of normality and our decision function.
Random partitioning produces noticeably shorter paths for anomalies. Hence, when a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies.
Read more in the User Guide.
New in version 0.18.
Parameters: 


Attributes: 

[1]  Liu, Fei Tony, Ting, Kai Ming and Zhou, ZhiHua. “Isolation forest.” Data Mining, 2008. ICDM‘08. Eighth IEEE International Conference on. 
[2]  Liu, Fei Tony, Ting, Kai Ming and Zhou, ZhiHua. “Isolationbased anomaly detection.” ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3. 
decision_function (X)  Average anomaly score of X of the base classifiers. 
fit (X[, y, sample_weight])  Fit estimator. 
fit_predict (X[, y])  Performs outlier detection on X. 
get_params ([deep])  Get parameters for this estimator. 
predict (X)  Predict if a particular sample is an outlier or not. 
score_samples (X)  Opposite of the anomaly score defined in the original paper. 
set_params (**params)  Set the parameters of this estimator. 
__init__(n_estimators=100, max_samples=’auto’, contamination=’legacy’, max_features=1.0, bootstrap=False, n_jobs=None, behaviour=’old’, random_state=None, verbose=0)
[source]
decision_function(X)
[source]
Average anomaly score of X of the base classifiers.
The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of a n_left samples isolation tree is added.
Parameters: 


Returns: 

estimators_samples_
The subset of drawn samples for each base estimator.
Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the inbag samples.
Note: the list is recreated at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.
fit(X, y=None, sample_weight=None)
[source]
Fit estimator.
Parameters: 


Returns: 

fit_predict(X, y=None)
[source]
Performs outlier detection on X.
Returns 1 for outliers and 1 for inliers.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Predict if a particular sample is an outlier or not.
Parameters: 


Returns: 

score_samples(X)
[source]
Opposite of the anomaly score defined in the original paper.
The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest.
The measure of normality of an observation given a tree is the depth of the leaf containing this observation, which is equivalent to the number of splittings required to isolate this point. In case of several observations n_left in the leaf, the average path length of a n_left samples isolation tree is added.
Parameters: 


Returns: 

set_params(**params)
[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


sklearn.ensemble.IsolationForest
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html