Feature engineering with scikit-learn

The example notebook on this page illustrates how to use scikit-learn on Databricks for feature engineering.

Use scikit-learn with MLflow integration on Databricks

This notebook shows a complete end-to-end example of loading data, training a model, distributed hyperparameter tuning, and model inference. It also illustrates how to use MLflow and the model registry.

Note

The following notebook may include functionality that is not available in this release of Databricks on Google Cloud.

If your workspace is enabled for Unity Catalog, use this version of the notebook:

Use scikit-learn with MLflow integration on Databricks (Unity Catalog)

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If your workspace is not enabled for Unity Catalog, use this version of the notebook:

Use scikit-learn with MLflow integration on Databricks

Open notebook in new tab Copy link for import