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.

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.

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.

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.

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.

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.

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.