Logo
1.9.3

Release Notes

  • Change Log

Introduction

  • Introduction to Driverless AI

Licensing

  • Driverless AI License

Installation and Upgrade

  • Driverless AI Installation And Upgrade

Configuration

  • Configuration and Authentication

Datasets

  • Datasets in Driverless AI

Data Insights

  • Automatic Visualization

Feature Engineering

  • Automatic Feature Engineering

Modeling

  • Building Models in Driverless AI
  • Automated Model Documentation (AutoDoc)

Machine Learning Interpretability

  • Machine Learning Interpretability
    • MLI Overview
    • The Interpreted Models Page
    • MLI for Regular (Non-Time-Series) Experiments
      • Interpreting a Model
      • Interpretation Expert Settings
      • Explainer (Recipes) Expert Settings
      • Understanding the Model Interpretation Page
      • Viewing Explanations
      • General Considerations
    • MLI for Time-Series Experiments
    • MLI Custom Recipes

Scoring on New Datasets

  • Scoring on Another Dataset

Transforming Datasets

  • Transforming Another Dataset

Scoring Pipelines

  • Scoring Pipelines

Productionization

  • Deploying the MOJO Pipeline

Clients

  • Driverless AI Clients

Monitoring and Logging

  • Monitoring and Logging

Security

  • Security

Frequently Asked Questions

  • FAQ

Appendices

  • Appendix A: Custom Recipes
  • Appendix B: Third-Party Integrations

References

  • References

Third-Party Notices

  • Third-Party Licenses
Using Driverless AI
  • »
  • Machine Learning Interpretability »
  • MLI for Regular (Non-Time-Series) Experiments
  • Edit on GitHub

MLI for Regular (Non-Time-Series) ExperimentsΒΆ

This section describes MLI functionality and features for regular experiments. Refer to MLI for Time-Series Experiments for MLI information with time-series experiments.

  • Interpreting a Model
    • Interpreting a Driverless AI Model
    • Interpreting Predictions from an External Model
    • Recipes
    • Interpretation Expert Settings
  • Interpretation Expert Settings
    • MLI Tab
    • AutoDoc Tab
  • Explainer (Recipes) Expert Settings
    • Disparate Impact Analysis Explainer Settings
    • Partial Dependence Plot Explainer Settings
    • Shapley Summary Plot Explainer Settings
    • Surrogate Decision Tree Explainer Settings
  • Understanding the Model Interpretation Page
    • Summary Tab
    • Interpretations using Driverless AI Model (DAI Model Tab)
    • Interpretations using Surrogate Model (Surrogate Model Tab)
    • Interpretations using NLP Dataset (Dataset Tab)
    • Dashboard
    • Actions Button
    • The Task Bar
    • DAI Model Plots
    • Surrogate Model Plots
  • Viewing Explanations
  • General Considerations
    • Machine Learning and Approximate Explanations
    • The Multiplicity of Good Models in Machine Learning
    • Expectations for Consistency Between Explanatory Techniques
Next Previous

© Copyright 2017-2021 H2O.ai. Last updated on Jun 03, 2021.

Built with Sphinx using a theme provided by Read the Docs.