For local queries, row and Shapley data are now displayed in tandem.Īdded the ability to navigate to dataset and experiment in MLI.Ĭonsolidated all MLI explainer logs into one zip file for download.Īdded support for bulk abort of multiple experiments in a project. For details, see Driverless AI user settings.Īdded ‘feature_store_mojo’ recipe type to create a MOJO to be used as a feature engineering pipeline in the H2O Feature Store.Īdded the ability to run Disparate Impact Analysis on external datasets. For details, see expert-settings-navigate.Īdded user preferences section for per-user data connector setup. If undetected, can mislead validation.Īdded support for prediction intervals for regression experiments in Java MOJO scoring (for both C++ and Java MOJO runtimes). Option to either drop duplicate rows or convert them into single weighted rows.Īdded detection of joint rows in training, validation and testing datasets after data preparation, before modeling. Only enabled by default for GLM/TensorFlow/TorchGrowNet and FTRL models at high interpretability.Īdded improved handling of duplicate rows in training data (after dropping columns to drop). For cases where target encoding isn’t allowed due to higher interpretability requirements, the highly interpretable BinnerTransformer can help create more accurate models. Given bins, a numeric column is converted into multiple output columns by using either piece-wise linear encoding or binary encoding. Uses tree splits (default) or quantiles to create bins, and can automatically reduce the number of bins based on their predictive power. For more information, see Leaderboard Wizard: Business value calculator.Īdded repeated cross-validation for final ensembles for small data, resulting in improved accuracy.Īdded the BinnerTransformer for one-dimensional binning of numeric features. Perform a business value analysis for all models in a project by clicking the Analyze button on the Project page (only for classification experiments). For more information, see Dataset Join Wizard. Join two datasets by clicking on a specific dataset and then clicking Join Wizard. For more information, see Driverless AI Experiment Setup Wizard.ĭataset Join Wizard. Configure and start experiments by clicking on a specific dataset and then clicking Predict Wizard. Deploying Driverless AI Models to ProductionĮxperiment Wizard.Automated Model Documentation (AutoDoc).Driverless AI License and Version Support.
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