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Isolation Forest


Isolation forests (IF) are tree based models specifically used for outlier detection. The IF isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. 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 random trees, is a measure of normality and is used to define an anomaly score. Outliers can typically be isolated quicker, leading to shorter paths. The algorithm is suitable for low to medium dimensional tabular data.




  • threshold: threshold value for the outlier score above which the instance is flagged as an outlier.

  • n_estimators: number of base estimators in the ensemble. Defaults to 100.

  • max_samples: number of samples to draw from the training data to train each base estimator. If int, draw max_samples samples. If float, draw max_samples times number of features samples. If ‘auto’, max_samples = min(256, number of samples).

  • max_features: number of features to draw from the training data to train each base estimator. If int, draw max_features features. If float, draw max_features times number of features features.

  • bootstrap: whether to fit individual trees on random subsets of the training data, sampled with replacement.

  • n_jobs: number of jobs to run in parallel for fit and predict.

  • data_type: can specify data type added to metadata. E.g. ‘tabular’ or ‘image’.

Initialized outlier detector example:

from alibi_detect.od import IForest

od = IForest(


We then need to train the outlier detector. The following parameters can be specified:

  • X: training batch as a numpy array.

  • sample_weight: array with shape (batch size,) used to assign different weights to each instance during training. Defaults to None.

It is often hard to find a good threshold value. If we have a batch of normal and outlier data and we know approximately the percentage of normal data in the batch, we can infer a suitable threshold:



We detect outliers by simply calling predict on a batch of instances X to compute the instance level outlier scores. We can also return the instance level outlier score by setting return_instance_score to True.

The prediction takes the form of a dictionary with meta and data keys. meta contains the detector’s metadata while data is also a dictionary which contains the actual predictions stored in the following keys:

  • is_outlier: boolean whether instances are above the threshold and therefore outlier instances. The array is of shape (batch size,).

  • instance_score: contains instance level scores if return_instance_score equals True.

preds = od.predict(



Outlier detection on KDD Cup 99