H2O Automated Model Documentation (AutoDoc) automatically creates model documentation for supervised learning models created in H2O-3 and Scikit-Learn. Automated documentation is used in production in H2O Driverless AI. This industry-leading capability is now available as a new commercial module.
Create comprehensive, high quality model documentation in minutes
- Distributed Automatic document generation in Microsoft Word (docx) or Markup (.md) formats.
- Out-of-the-box documentation template included
- Template customization available to fit with your organization’s standards and requirements
- Support for models generated in H20-3 and Scikit-Learn
- Support for H2O-3: Deep Learning, Random Forest, GLM, Gradient Boosted Machines, Stacked Ensembles, and XGBoost models
AutoDoc generates an editable Word document based on an automated template that includes:
- Experiment Overview to provide an overview of the modeling problem
- System Specifications to describe the exact configuration of the system that produced the model including the version of H20-3 or Scikit-learn that was used.
- Data Overview including information on the data shape and summary statistics for each feature (numeric and categorical values)
- Data Shift to highlight any difference between training and validation data.
- Validation Strategy
- Model Parameters and Values
- Common Classification or Regression Metrics
- Population Stability Index
- Prediction Statistics for training and validation
- Feature Importance using H2O native importance or Shapely Importance
- Response Rate by Quantile
- Actual vs. Predicted Probabilities
- Partial Dependence Plots
- Alternative Models Summary to show the other techniques and their parameters that were tested against the winning model