Compare model types with Hyperopt and MLflow

Note

The open-source version of Hyperopt is no longer being maintained.

Hyperopt is not included in Databricks Runtime for Machine Learning after 16.4 LTS ML. Azure Databricks recommends using either Optuna for single-node optimization or RayTune for a similar experience to the deprecated Hyperopt distributed hyperparameter tuning functionality. Learn more about using RayTune on Azure Databricks.

This notebook demonstrates how to tune the hyperparameters for multiple models and arrive at a best model overall. It uses Hyperopt with SparkTrials to compare three model types, evaluating model performance with a different set of hyperparameters appropriate for each model type.

Compare models using scikit-learn, Hyperopt, and MLflow notebook

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