Overview

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

Key Capabilities

  • 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 H2O-3 and Scikit-Learn
  • Support for H2O-3: Deep Learning, Random Forest, GLM, Gradient Boosted Machines, Stacked Ensembles, and XGBoost models
See an H2O AutoDoc Example

Documentation Features

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 H2O-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
  • datasets
  • 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