How do you get a feature important in Isolation Forest?

So here’s what you can do to get feature importances:

1. Determine a threshold for decision path length.
2. For each event, loop through the trees in the iForest and select paths that are shorter than the threshold path length.

What is decision function in Isolation Forest?

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.

When to use the output of isolation forest?

Having said that, If you are very confident about the results of Isolation Forest classifier and you have a capacity to train another model then you could use the output of Isolation Forest i.e -1/1 values as target-class to train a Random Forest classifier. This will give you feature importance for detecting anomaly.

How to fit isolation forest in scikit learn?

In an unsupervised setting for higher-dimensional data (e.g. 10 variables (numerical and categorical), 5000 samples, ratio of anomalies likely 1% or below but unknown) I am able to fit the isolation forest and retrieve computed anomaly scores (following the original paper and using the implementation in scikit-learn ).

How to calculate the isolation of a tree?

Calculate some kind of ‘isolation metric’ for each node in each tree (such as % of samples split) and get an average of this metric for each splitting feature. After the model has been fit, go through each feature one at a time, randomly permute the data for that feature, and calculate the anomaly scores.

Are there disadvantages to using sklearn in isolation forest?

If you’re using sklearn or other Python based implementations, the biggest disadvantage to this technique is speed. It takes a while to root through all the trees, and if you’re interested in global importances you’ll have to loop though all the events as well.

How do you get a feature important in isolation Forest?

So here’s what you can do to get feature importances:

1. Determine a threshold for decision path length.
2. For each event, loop through the trees in the iForest and select paths that are shorter than the threshold path length.

What is isolation forest used for?

Isolation forest works on the principle of the decision tree algorithm. It isolates the outliers by randomly selecting a feature from the given set of features and then randomly selecting a split value between the maximum and minimum values of the selected feature.

What does an isolation Forest create?

Brief description. Isolation Forest builds an ensemble of Binary Trees for a given dataset. Anomalies, due to their nature, they have the shortest path in the trees than normal instances.

What is the difference between isolation forest and random forest?

Isolation Forest is similar in principle to Random Forest and is built on the basis of decision trees. Isolation Forest, however, identifies anomalies or outliers rather than profiling normal data points. Random partitioning produces noticeably shorter paths for anomalies.

How to calculate feature importance in an isolation forest?

As of scikit-learn version 0.19.1, there is no implementation for calculating feature importance in an Isolation Forest. I’m also having trouble finding any online resources proposing ways to get at the problem.

How to fit isolation forest in scikit learn?

In an unsupervised setting for higher-dimensional data (e.g. 10 variables (numerical and categorical), 5000 samples, ratio of anomalies likely 1% or below but unknown) I am able to fit the isolation forest and retrieve computed anomaly scores (following the original paper and using the implementation in scikit-learn ).

How to calculate the isolation of a tree?

Calculate some kind of ‘isolation metric’ for each node in each tree (such as % of samples split) and get an average of this metric for each splitting feature. After the model has been fit, go through each feature one at a time, randomly permute the data for that feature, and calculate the anomaly scores.

Are there disadvantages to using sklearn in isolation forest?

If you’re using sklearn or other Python based implementations, the biggest disadvantage to this technique is speed. It takes a while to root through all the trees, and if you’re interested in global importances you’ll have to loop though all the events as well.