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On-premise users: click in-app to access the full platform documentation for your version of DataRobot.

Advanced options

After importing data and selecting a target variable, the Data page appears. From this page you can click the Show advanced options link to access advanced modeling parameters.

These parameters are summarized in the following sections:

Option Description
Additional Set additional parameters and modify values that can effect model builds.
Bias and Fairness Set conditions that help calculate fairness, as well as identify and attempt to mitigate bias in a model's predictive behavior.
Clustering Set the number of clusters that DataRobot discovers in a time series clustering project.
External model prediction insights Bring external model(s) into the DataRobot AutoML environment, view them on the Leaderboard, and run a subset of DataRobot's evaluative insights for comparison against DataRobot models.
Feature Constraints Set monotonic constraints to control the influence between variables and target.
Partitioning Set how data is partitioned for training/validation/holdout and the validation type.
Partitioning: Date/time Set how data is partitioned for OTV or time series projects.
Smart Downsampling Downsample the majority class for faster model build time.
Time Series Set a variety or time series-specific advanced options.
Train-time image augmentation Create new training images to increase the amount of training data.

Updated September 27, 2022