H2O Isolation Forest for Outlier Detection Explained by Shapley Values

This tutorial shows how to train an H2O Model in KNIME. We will train an Isolation Forest Model to detect frauds, i.e. outliers or anomalies
1. Prepare: Load the data and import the resulting KNIME Table to H2O.
2. Learn: We learn the Isolation Forest Model using the H2O Isolation Forest Learner. We want H2O to build 100 trees. All other model parameters are H2Os defaults.
3. Predict: Make predictions on the same data using your model(s). In the output there will be the predictions (normalized anomaly score) and mean lengths of the predicted decision tree paths.For further processing we convert the H2O Frame back to table.
4. Classify: If we know that about 5 percent of our data rows are anomalies, we can calculate the 95th quantile. This quantile can be used as a threshold by the Rule Engine node to classify each row either as an anomaly or not.
5. Explain: We use Shapley Values to explain only the ones predicted as anomalies/outliers.

External resources

• Interpretable Machine Learning - A Guide for Making Black Box Models Explainable. - Christoph Molnar - 2019-12-12
• H2O Isolation Forest documentation