Modeling
The sections described below provide information to help you easily navigate the ML modeling process.
Overview
Topic
Describes...
Fundamentals of modeling
Understand the types of ML modeling projects you can create in DataRobot. Learn the general process of modeling, analyzing, and selecting models for deployment.
DataRobot workflow overview
A quick overview of steps from data ingest to model deployment. Each step links to the full documentation for more complete detail.
Build models
Model insights (Leaderboard tabs)
Topic
Describes...
Evaluate tabs
View key plots and statistics needed to judge and interpret a model’s effectiveness.
Understand tabs
Understand what drives a model’s predictions.
Describe tabs
View model building information and feature details.
Predict tabs
Make predictions in DataRobot using the UI or API.
Compliance tabs
Compile model development documentation that can be used for regulatory validation.
Comments tab
Add comments to assets in the AI Catalog .
Bias and Fairness tabs
Identify if a model is biased and why the model is learning bias from the training data.
Other model tabs
Compare models across a project.
Specialized workflows
Time series modeling
Business operations
Topic
Describes...
No-Code AI Apps
Build and configure AI-powered applications using a no-code interface.
Partner applications
DataRobot applications developed for partner integrations.
Use case tracking
Measure success by defining expected, and tracking actual, model value in real time.
Modeling reference
Topic
Describes...
Data and sharing
Dataset requirements, sharing assets, and permissions.
Modeling details
The Leaderboard and the processes that drive model building, including partitioning and feature derivation.
Eureqa advanced tuning
Tune Eureqa models by modifying building blocks, customizing the target expression, and modifying other model parameters.
Updated June 1, 2022
Submit
Thanks for your feedback!