Isolation Forest (Anomaly Detection)

Isolation forest is an anomaly detection algorithm. It isolates observations by creating a Random Forest of trees, each splitting samples in different partitions. Anomalies tend to have much shorter paths from the root of the tree. Thus, the mean distance from the root provides a good measure of non-normality.

Number of trees

Number of trees in the forest.

Contamination

Expected proportion of anomalies in the data.

Use "auto" contamination

Let the threshold be determined by the algorithm.

Max sample proportion

Proportion of the data to use to build each tree.

Use bootstrap

Use bootstrapping to sample from the data.

Max feature proportion

Proportion of features to test for each split.

Anomalies to display

Maximum number of anomalies to display in the model report. Too high a number may cause memory and UI problems.

Seed

Used to generate reproducible results. 0 or no value means that no known seed is used (results will not be fully reproducible).

Parallelism

Number of cores used for parallel training. Using more cores leads to faster training but at the expense of more memory consumption, especially for large training datasets.

Allow sparse matrices

Allow DSS to use sparse matrices to train the model
This may help reduce RAM and CPU usage