Modeling details¶
This section provides details into components of the functionality that makes up the model building process.
Topic | Description |
---|---|
Data | |
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 | |
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. |
Miscellaneous | |
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 January 30, 2025
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