# Predictive AI

> Predictive AI - Provides a variety of reference material for model building, model insights, and
> specialized workflows.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-01T23:10:48.114689+00:00` (UTC).

## Primary page

- [Predictive AI](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/index.html): Full documentation for this topic (HTML).

## Related documentation

- [Reference documentation](https://docs.datarobot.com/en/docs/reference/index.html): Linked from this page.
- [Data partitioning](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/data-partitioning.html): Linked from this page.
- [Feature lists](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/custom-list-ref.html): Linked from this page.
- [Modeling algorithms](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-list.html): Linked from this page.
- [Modeling process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-ref.html): Linked from this page.
- [Model recommendation process](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/model-rec-process.html): Linked from this page.
- [Leaderboard reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/leaderboard-ref.html): Linked from this page.
- [Optimization metrics](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/opt-metric.html): Linked from this page.
- [SHAP reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/shap-ref.html): Linked from this page.
- [Feature Associations](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/feature-associate.html): Linked from this page.
- [Insurance-specific settings](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/insurance-settings.html): Linked from this page.
- [advanced settings](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-adv-experiment.html): Linked from this page.
- [Sliced insights](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/sliced-insights.html): Linked from this page.
- [Bias and Fairness reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/bias-ref.html): Linked from this page.
- [GA2M output](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ga2m.html): Linked from this page.
- [Time series reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ts-reference/index.html): Linked from this page.
- [Eureqa advanced tuning](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/eureqa-ref/index.html): Linked from this page.
- [Composable ML reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/index.html): Linked from this page.
- [Visual AI reference](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/vai-reference/index.html): Linked from this page.
- [Export charts and data](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/export-results.html): Linked from this page.
- [Worker queue (NextGen)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/wb-troubleshooting.html): Linked from this page.
- [Worker queue (Classic)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/worker-queue.html): Linked from this page.
- [XEMP qualitative strength](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/xemp-calc.html): Linked from this page.
- [AI Report (Classic only)](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/ai-report.html): Linked from this page.

## Documentation content

# Predictive AI reference

The following sections provide reference content that supports working with predictive and time-aware experiments:

| Topic | Description |
| --- | --- |
| Data partitioning | Describes validation types and data partitioning methods. |
| Feature lists | Shows details of working with DataRobot-generated and custom feature lists, as well as where in the platform you can create and manage them. |
| Modeling algorithms | Lists supervised and unsupervised modeling algorithms supported by DataRobot. |
| Modeling process | Describes modeling modes, two-stage models, and data summary information. |
| Model recommendation process | Describes the steps involved in DataRobot's selection of a recommended model. |
| Leaderboard reference | Provides a reference table of the badges that display in the Leaderboard and the Blueprint repository, model icons, and other Leaderboard indicators. |
| Optimization metrics | Briefly describes all metrics available for model building. |
| SHAP reference | Provides details of SHapley Additive exPlanations, the coalitional game theory framework. |
| Feature Associations | Explains about associations, understanding the mutual information and Cramer's V metrics, and how associations are calculated. |
| Insurance-specific settings | Describes Exposure, Count of events, and Offset options, configured in advanced settings. |
| Sliced insights | Describes sliced insights where you can view and compare insights based on segments of a project’s data. |
| Bias and Fairness reference | Provides an overview of the methods used to calculate fairness and to identify biases in the model's predictive behavior. |
| GA2M output | Describes and helps to understand the output for Generalized Additive Model (GA2M) models, available as a download from the Rating Tables tab. |
| Time series reference | Provides reference material explaining the DataRobot framework for implementing time series modeling and see a variety of deep-dive reference material for DataRobot time series modeling. |
| Eureqa advanced tuning | Describes how to modify building blocks, customize the target expression, and modify other model parameters for Eureqa models. |
| Composable ML reference | Provides information on blueprints in the AI Catalog, model metadata, feature considerations, and a sentiment analysis example. |
| Visual AI reference | Provides workflow and reference materials for including images as part of your DataRobot experiments. |
| Export charts and data | Explains about downloading created insights. |
| Worker queue (NextGen) | Helps to understand modeling workers and how to troubleshoot issues in NextGen. |
| Worker queue (Classic) | Helps to understand modeling workers and how to troubleshoot issues in Classic. |
| XEMP qualitative strength | Describes the calculations used to determine XEMP qualitative strength. |
| AI Report (Classic only) | Describes how to create a report of modeling results and insights. |