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# `sklearn.ensemble`.IsolationForest

class sklearn.ensemble.IsolationForest(*, n_estimators=100, max_samples='auto', contamination='auto', max_features=1.0, bootstrap=False, n_jobs=None, random_state=None, verbose=0, warm_start=False)

  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:
    n_estimators int, default=100

      The number of base estimators in the ensemble.

    max_samples“auto”, int or float, 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).

    contamination‘auto’ or float, default=’auto’

      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 int or float, 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(1, int(max_features * n_features_in_))` features.

      Note: using a float number less than 1.0 or integer less than number of features will enable feature subsampling and leads to a longer runtime.

    bootstrap bool, 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.

    n_jobs int, default=None

      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 Glossary for more details.

    random_state int, RandomState instance or None, default=None

      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 Glossary.

    verbose int, default=0

      Controls the verbosity of the tree building process.

    warm_start bool, default=False

      When set to `True`, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See the Glossary.

      New in version 0.21.

  Attributes:
    estimator_`ExtraTreeRegressor` instance

      The child estimator template used to create the collection of fitted sub-estimators.

      New in version 1.2: `base_estimator_` was renamed to `estimator_`.

    `base_estimator_`ExtraTreeRegressor instance

      Estimator used to grow the ensemble.

    estimators_list of ExtraTreeRegressor instances

      The collection of fitted sub-estimators.

    estimators_features_list of ndarray

      The subset of drawn features for each base estimator.

    `estimators_samples_`list of ndarray

      The subset of drawn samples for each base estimator.

    max_samples_int

      The actual number of samples.

    offset_float

      Offset used to define the decision function from the raw scores. We have the relation: `decision_function = score_samples - offset_`. `offset_` is defined as follows. When the contamination parameter is set to “auto”, the offset is equal to -0.5 as the scores of inliers are close to 0 and the scores of outliers are close to -1. When a contamination parameter different than “auto” is provided, the offset is defined in such a way we obtain the expected number of outliers (samples with decision function < 0) in training.

      New in version 0.20.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

  See also

  `sklearn.covariance.EllipticEnvelope`

    An object for detecting outliers in a Gaussian distributed dataset.

  `sklearn.svm.OneClassSVM`

    Unsupervised Outlier Detection. Estimate the support of a high-dimensional distribution. The implementation is based on libsvm.

  `sklearn.neighbors.LocalOutlierFactor`

    Unsupervised Outlier Detection using Local Outlier Factor (LOF).

  Notes

  The implementation is based on an ensemble of ExtraTreeRegressor. The maximum depth of each tree is set to `ceil(log_2(n))` where \(n\) is the number of samples used to build the tree (see (Liu et al., 2008) for more details).

  References

  [1]

  Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation forest.” Data Mining, 2008. ICDM’08. Eighth IEEE International Conference on.

  [2]

  Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. “Isolation-based anomaly detection.” ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.

  Examples

      >>> from sklearn.ensemble import IsolationForest
      >>> X = [[-1.1], [0.3], [0.5], [100]]
      >>> clf = IsolationForest(random_state=0).fit(X)
      >>> clf.predict([[0.1], [0], [90]])
      array([ 1,  1, -1])

  Methods

  `decision_function`(X) | Average anomaly score of X of the base classifiers.
  `fit`(X[, y, sample_weight]) | Fit estimator.
  `fit_predict`(X[, y]) | Perform fit on X and returns labels for X.
  `get_metadata_routing`() | Get metadata routing of this object.
  `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_fit_request`(*[, sample_weight]) | Request metadata passed to the `fit` method.
  `set_params`(**params) | Set the parameters of this estimator.

  property base_estimator_

    Estimator used to grow the ensemble.

  decision_function(X)

    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:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

    Returns:
      scores ndarray of shape (n_samples,)

        The anomaly score of the input samples. The lower, the more abnormal. Negative scores represent outliers, positive scores represent inliers.

  property 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 in-bag samples.

    Note: the list is re-created 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)

    Fit estimator.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Use `dtype=np.float32` for maximum efficiency. Sparse matrices are also supported, use sparse `csc_matrix` for maximum efficiency.

      y Ignored

        Not used, present for API consistency by convention.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights. If None, then samples are equally weighted.

    Returns:
      self object

        Fitted estimator.

  fit_predict(X, y=None)

    Perform fit on X and returns labels for X.

    Returns -1 for outliers and 1 for inliers.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples.

      y Ignored

        Not used, present for API consistency by convention.

    Returns:
      y ndarray of shape (n_samples,)

        1 for inliers, -1 for outliers.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    Predict if a particular sample is an outlier or not.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples. Internally, it will be converted to `dtype=np.float32` and if a sparse matrix is provided to a sparse `csr_matrix`.

    Returns:
      is_inlier ndarray of shape (n_samples,)

        For each observation, tells whether or not (+1 or -1) it should be considered as an inlier according to the fitted model.

  score_samples(X)

    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:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        The input samples.

    Returns:
      scores ndarray of shape (n_samples,)

        The anomaly score of the input samples. The lower, the more abnormal.

  set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → IsolationForest

    Request metadata passed to the `fit` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `fit` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `fit`.

    - `None`: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

    - `str`: metadata should be passed to the meta-estimator with this given alias instead of the original name.

    The default (`sklearn.utils.metadata_routing.UNCHANGED`) retains the existing request. This allows you to change the request for some parameters and not others.

    New in version 1.3.

    Note

    This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a `Pipeline`. Otherwise it has no effect.

    Parameters:
      sample_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `fit`.

    Returns:
      self object

        The updated object.

  set_params(**params)

    Set the parameters of this estimator.

    The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `<component>__<parameter>` so that it’s possible to update each component of a nested object.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

## Examples using `sklearn.ensemble.IsolationForest`

IsolationForest example

IsolationForest example

Comparing anomaly detection algorithms for outlier detection on toy datasets

Comparing anomaly detection algorithms for outlier detection on toy datasets

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators