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1.10.7.3

Release Notes

  • Change Log
  • Driverless AI release blogs

Introduction

  • Introduction to Driverless AI

Licensing and Version Support

  • Driverless AI License and Version Support

Installation and Upgrade

  • Driverless AI Installation and Upgrade

Configuration

  • Configuration and Authentication

Datasets

  • Datasets in Driverless AI

Data Insights

  • Automatic Visualization

Custom Recipes

  • Custom Recipe Management
  • Custom Individual Recipe

Feature Engineering

  • Automatic Feature Engineering
  • Feature Count Control

Modeling

  • Building Models in Driverless AI
  • H2O Driverless AI Experiment Setup Wizard
  • Automated Model Documentation (AutoDoc)

Machine Learning Interpretability

  • Machine Learning Interpretability
    • MLI Overview
    • The Interpreted Models Page
    • MLI for Regular (Non-Time-Series) Experiments
      • Interpret 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

Generative AI

  • h2oGPT integration

Scoring on New Datasets

  • Scoring on Another Dataset

Transforming Datasets

  • Transform datasets

Scoring Pipelines

  • Scoring Pipelines

Productionization

  • Deploy Driverless AI models

Clients

  • Driverless AI Clients

Monitoring and Logging

  • Monitoring and Logging

Security

  • Security

Frequently Asked Questions

  • FAQ

Appendices

  • Appendix A: Third-Party Integrations

References

  • References

Third-Party Notices

  • Third-Party Licenses

Translations

  • Go to User Guide in Chinese
  • Go to User Guide in Korean
Using Driverless AI
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  • 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.

  • Interpret a model
    • Interpret a Driverless AI model
    • Interpreting Predictions From an External Model
    • Explainer Recipes
    • Interpretation Expert Settings
  • Interpretation Expert Settings
    • MLI Tab
    • MLI NLP Tab
    • MLI Surrogate Models Tab
  • Explainer (Recipes) Expert Settings
    • Absolute Permutation Feature Importance Explainer Settings
    • MLI AutoDoc Explainer Settings
    • Disparate Impact Analysis Explainer Settings
    • NLP Partial Dependence Plot Explainer Settings
    • NLP Vectorizer + Linear Model Text Feature Importance Explainer Settings
    • Partial Dependence Plot Explainer Settings
    • Sensitivity Analysis Explainer Settings
    • Shapley Summary Plot Explainer Settings
    • Shapley Values for Original Features 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 (NLP Tab)
    • MLI Dashboard
    • The Task Bar
    • DAI Model Plots
    • Surrogate Model Plots
    • NLP Plots
  • Viewing Explanations
  • General Considerations
    • Machine Learning and Approximate Explanations
    • The Multiplicity of Good Models in Machine Learning
    • Expectations for Consistency Between Explanatory Techniques
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