Python code examples¶
The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of common data science and machine learning workflows. Be sure to review the API quickstart guide before using the notebooks below.
|Modeling workflow overview
|How to use DataRobot's Python client to train and experiment with models.
|Feature selection notebooks
|Notebooks that outline Feature Importance Rank Ensembling (FIRE) and advanced feature selection with Python.
|Build a model factory
|A system or a set of procedures that automatically generate predictive models with little to no human intervention.
|Make Visual AI predictions via the API
|Scripting code for making batch predictions for a Visual AI model via the API.
|Using the Batch Prediction API
|DataRobot's batch prediction API to score large datasets with a deployed DataRobot model.
|Configure datetime partitioning
|How to use datetime partitioning to guard a project against time-based target leakage.
|Create and schedule JDBC prediction jobs
|How to use DataRobot's Python client to schedule prediction jobs and write them to a JDBC database.
|How to transfer models from one DataRobot cluster to another as an .mlpkg file.
|Make batch predictions with Azure Blob storage
|How to generate SHAP-based Prediction Explanations with a use case that determines what drives home value in Iowa.
|Make batch predictions with Google Cloud Storage
|How to read input data from and write predictions back to Google Cloud Storage.