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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

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

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

Topic Describes...
Date/time partitioning Build models with time-relevant data (not time series).
Unsupervised learning Work with unlabeled or partially labeled data to build anomaly detection or clustering models.
Composable ML Build blueprints using built-in DataRobot tasks and custom Python or R code.
Visual AI Use image-based datasets.
Location AI Use geospatial datasets.

Time series modeling

Topic Describes...
Workflow overview Basic time series modeling workflow.
Date/time partitioning Build models with time-relevant data.
Time series modeling Build time series forecasting models.
Multiseries modeling Build models with datasets that contain multiple time series based on a common set of input features.
Nowcasting Predict the current value of a target based on past and present.
External prediction comparison Use 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 View deep-dive reference material for DataRobot 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
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