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Modeling details

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

Topic Description
Exploratory Data Analysis Details of Exploratory Data Analysis (EDA), phases 1 and 2.
Data partitioning and validation Describes validation types and data partitioning methods.
Modeling algorithms List of supervised and unsupervised modeling algorithms supported by DataRobot.
Modeling process details Bits and pieces of the initial model building process.
GPU support Enabling GPU support to improve runtime for deep learning models.
Leaderboard reference Components of the Leaderboard, blender models, and asterisked scores.
Model recommendation process Steps involved in DataRobot's selection of a recommended model.
Sliced insights View and compare insights based on segments of a project’s data.
SHAP reference Details of SHapley Additive exPlanations, the coalitional game theory framework.
XEMP calculations Describes the calculations used to determine XEMP qualitative strength.
Optimization metrics Short descriptions of all metrics available for model building.
AI Report Create a report of modeling results and insights.
Export charts and data Download created insights.
Worker Queue Manage models and projects and export data.

Updated June 24, 2024