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Modeling

The Modeling section provides information to help you easily navigate the modeling process. Modeling begins after you have loaded, transformed, performed feature discovery, and analyzed your data.

Workflow overview

The UI-based Workflow overview: AutoML provides a quick overview of steps from data ingest to model deployment. Each step links back to the full documentation for more complete detail.

Build models

Topic Describes...
Advanced options Setting advanced modeling parameters prior to building.
Build models Elements of the basic modeling workflow.
Manage projects Managing models and projects and export data.

Model insights (Leaderboard tabs)

Topic Describes...
Evaluate tabs Viewing key plots and statistics needed to judge and interpret a model’s effectiveness.
Understand tabs Explaining what drives a model’s predictions.
Describe tabs Viewing model building information and feature details.
Predict tabs Making predictions in DataRobot using the UI or API.
Compliance tabs Compiling model development documentation that can be used for regulatory validation.
Comments tab Adding comments or host a discussion around any item in the AI Catalog.
Bias and Fairness tabs Identifying if a model is biased and why the model is learning bias from the training data.
Other model tabs Comparing models across a project.

Specialized workflows

Topic Describes...
Date/time partitioning Building models with time-relevant data (not time series).
Unsupervised learning Working with unlabeled or partially labeled data and detect abnormalities in your dataset.
Visual AI Using image-based datasets.
Location AI Using geospatial datasets.

Time series modeling

Topic Describes...
Workflow overview Basic time series modeling workflow.
Date/time partitioning Building models with time-relevant data.
Time series modeling Building time series forecasting models.
Multiseries modeling Building models with datasets that contain multiple time series based on a common set of input features.
Nowcasting Predicting the current value of a target based on past and present.
External prediction comparison Using the output of predictions made by non-DataRobot models as a baseline to compare against DataRobot predictions.
Modeling dataset The modeling dataset that is a result of the feature derivation process.
Time series reference Provides deep-dive reference material for DataRobot time series modeling.

Business operations

Topic Describes...
AI App Builder Building and configuring AI-powered applications using a no-code interface.
AI applications (deprecated) Enabling core services without building models and evaluating performance in DataRobot.
Partner applications DataRobot applications developed for partner integrations.
Use case tracking Measuring success by defining expected, and tracking actual, model value in real time.

Modeling reference

Topic Describes...
Data and sharing Partitioning, feature derivation, and sharing roles.
Modeling details The Leaderboard and the processes that drive model building.
Eureqa advanced tuning Tuning Eureqa models by modifying building blocks, customizing the target expression, and modifying other model parameters.

Updated November 30, 2021
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