---
title: Modeling details
description: This section introduces the model building process; data partitioning and validation; features for working with a project after model building completes, including Generate AI report, Export charts and data; and links to details on its component tasks.

---

# Modeling details {: #modeling-details }

This section provides details into components of the functionality that makes up the model building process.

Topic | Describes...
----- | ------
 **Data** |  :~~:  
[Exploratory Data Analysis](eda-explained) | Details of Exploratory Data Analysis (EDA), phases 1  and 2.
[Data partitioning and validation](data-partitioning) | Describes validation types and data partitioning methods.
 **Modeling** |  :~~:  
[Modeling algorithms](model-list) | List of supervised and unsupervised modeling algorithms supported by DataRobot.
[Modeling process details](model-ref) | Bits and pieces of the initial model building process.
[Leaderboard reference](leaderboard-ref) | Components of the Leaderboard, blender models, and asterisked scores.
[Model recommendation process](model-rec-process) | Steps involved in DataRobot's selection of a recommended model.
[Sliced insights](sliced-insights) |  View and compare insights based on segments of a project’s data.
[SHAP reference](shap) | Details of SHapley Additive exPlanations, the coalitional game theory framework.
[XEMP calculations](xemp-calc) | Describes the calculations used to determine XEMP qualitative strength.
 **Miscellaneous** |  :~~:  
[Optimization metrics](opt-metric) | Short descriptions of all metrics available for model building.
[Generate AI Report](generate-ai-report) | Create a report of modeling results and insights.
[Export charts and data](export-results) | Download created insights.
[Worker Queue](worker-queue) | Manage models and projects and export data.
