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 H20-3 and Scikit-Learn
  • Support for H2O-3: Deep Learning, Random Forest, GLM, Gradient Boosted Machines, Stacked Ensembles, and XGBoost models

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 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
  • 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