DataRobot implemented multiple approaches of feature reduction at different stages of the modeling life cycle. 1) during EDA1 (right after you upload the data to DataRobot), non-informative features such as those with too many unique values will be excluded automatically from the informative feature list; 2) after EDA2 (after you click "Start"), DataRobot will remove features with target leakage, i.e. features highly correlated with the target; AND features with ACE score < 0.0005, i.e. features that are marginally correlated with the target; 3) during model training/analysis stage, features with accumulative feature importance over 0.95 will be retained with redundant features removed; 4) some of the algorithms offer intrinsic feature reduction such as LASSO, ENET by shrinking coefficients to 0. 5) If you are using the Automated Feature Discovery functionality to generate additional features from a secondary dataset(s), DataRobot also does supervised feature reduction. Hope above helps. Always keep in mind that feature reduction or feature selection heavily depends on domain knowledge.
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