# Predictive AI
URL: https://docs.datarobot.com/en/docs/reference/pred-ai-ref/index.html

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

# 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. |

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