[SYSTEMDS-3166] Add builtin for anomaly detection via Isolation Forest #2421
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This patch promotes the existing Isolation Forest algorithm implementation from the staging phase to builtin status, with improvements. The implementation provides two main builtins, outlierByIsolationForest for training iForest models and outlierByIsolationForestApply for scoring samples based on trained models. Specifically, we optimized the algorithm with vectorized harmonic number computation for improved scalability. The patch extends test coverage in `staging/isolationForestTest.dml` with comprehensive tests, and Python API integration tests. Refer to JIRA for detailed discussions. Related to apache#1980 Co-authored-by: keremaras1 <[email protected]> Co-authored-by: denizzqq <[email protected]>
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This patch promotes the existing Isolation Forest algorithm implementation from the staging phase to builtin status, with improvements. The implementation provides two main builtins, outlierByIsolationForest for training iForest models and outlierByIsolationForestApply for scoring samples based on trained models. Specifically, we optimized the algorithm with vectorized harmonic number computation for improved scalability. The patch extends test coverage in
staging/isolationForestTest.dmlwith comprehensive tests, and Python API integration tests. Refer to JIRA for detailed discussions.Related to #1980