---
type: concept
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  contentHash: f8c748e3c45028facac93b71024ff72bac43a1298cd27723bdf5243266b95ed0
  pageDirectory: concepts
  sources:
    - hyperparameter-tuning-with-optuna-databricks-on-aws.md
  confidence: 0.8
  provenanceState: inferred
  freshnessStatus: unverified
  aliases:
    - hyperparameter-tuning-on-databricks
    - HTOD
    - AutoML and Hyperparameter Tuning on Databricks
  citations:
    - file: hyperparameter-tuning-with-optuna-databricks-on-aws.md
title: Hyperparameter tuning on Databricks
description: Integration of Optuna hyperparameter optimization within the Databricks platform for scalable machine learning
tags:
  - databricks
  - machine-learning
  - cloud
timestamp: "2026-06-19T19:08:48.308Z"
---

# Hyperparameter Tuning on Databricks

**Hyperparameter tuning on Databricks** refers to the process of systematically searching for optimal hyperparameter values for machine learning models using the Databricks platform. Databricks supports hyperparameter tuning through integration with [Optuna](/concepts/optuna.md), a popular hyperparameter optimization framework, enabling distributed and automated search across parameter spaces.

## Overview

Hyperparameter tuning involves selecting the best set of hyperparameters for a machine learning model to optimize performance on a validation set. On Databricks, this process can be performed using Optuna, which provides a flexible framework for defining search spaces, running trials, and tracking results. The platform's distributed computing capabilities allow tuning jobs to scale across multiple nodes, reducing the time required to find optimal configurations. ^[hyperparameter-tuning-with-optuna-databricks-on-aws.md]

## Optuna Integration

Databricks integrates with Optuna to provide a structured approach to hyperparameter optimization. The core workflow involves defining an objective function that accepts a `trial` object and returns a metric to optimize. The `trial` object provides `suggest` methods for defining the hyperparameter search space. ^[hyperparameter-tuning-with-optuna-databricks-on-aws.md]

### Defining the Objective Function

The objective function is the central component of an Optuna tuning study. It receives a `Trial` object and uses its suggest methods to propose hyperparameter values. The function trains a model with those values and returns a numeric score that Optuna uses to guide the search. ^[hyperparameter-tuning-with-optuna-databricks-on-aws.md]

```python
import sklearn

def objective(trial):
    # Invoke suggest methods of a Trial object to generate hyperparameters.
    regressor_name = trial.suggest_categorical('classifier', ['SVR', 'RandomForest'])
    if regressor_name == 'SVR':
        svr_c = trial.suggest_float('svr_c', 1e-10, 1e10, log=True)
        regressor_obj = sklearn.svm.SVR(C=svr_c)
    else:
        rf_max_depth = trial.suggest_int('rf_max_depth', 2, 32)
        regressor_obj = sklearn.ensemble.RandomForestRegressor(max_depth=rf_max_depth)
    
    X, y = sklearn.datasets.fetch_california_housing(return_X_y=True)
    X_train, X_val, y_train, y_val = sklearn.model_selection.train_test_split(X, y, random_state=0)
    regressor_obj.fit(X_train, y_train)
    y_pred = regressor_obj.predict(X_val)
    error = sklearn.metrics.mean_squared_error(y_val, y_pred)
    return error  # An objective value linked with the Trial object
```

^[hyperparameter-tuning-with-optuna-databricks-on-aws.md]

### Hyperparameter Suggestion Methods

Optuna's `Trial` object provides several `suggest` methods for defining the search space:

- **`suggest_categorical(name, choices)`**: For categorical hyperparameters, such as model type or activation function.
- **`suggest_float(name, low, high, log=False)`**: For continuous hyperparameters, with optional logarithmic scaling.
- **`suggest_int(name, low, high)`**: For integer hyperparameters, such as tree depth or number of layers.

These methods allow the framework to intelligently explore the parameter space using algorithms like [Tree-structured Parzen Estimator (TPE)](/concepts/hyperopt-tree-of-parzen-estimators-tpe-algorithm.md) or CMA-ES. ^[hyperparameter-tuning-with-optuna-databricks-on-aws.md]

## Running Tuning Studies on Databricks

On Databricks, hyperparameter tuning studies can be executed as notebook workflows or as [Databricks Jobs](/concepts/dbt-task-in-databricks-jobs.md). The platform's Apache Spark cluster provides distributed compute resources that can parallelize trial execution, significantly speeding up the tuning process for large search spaces.

### Key Considerations

- **Scalability**: Databricks clusters can scale horizontally to run multiple trials in parallel, reducing wall-clock time.
- **Tracking**: Optuna studies can be integrated with [MLflow](/concepts/mlflow.md) for experiment tracking, logging hyperparameters and metrics for each trial.
- **Persistence**: Study results can be persisted to a database or file system for later analysis and model deployment.

## Related Concepts

- [Optuna](/concepts/optuna.md) — The hyperparameter optimization framework integrated with Databricks.
- [MLflow](/concepts/mlflow.md) — Experiment tracking and model registry for logging tuning results.
- Automated Machine Learning (AutoML) — Automated model selection and tuning on Databricks.
- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md) — Scaling model training across multiple nodes.
- Model Selection — The broader process of choosing the best model and hyperparameters.

## Sources

- hyperparameter-tuning-with-optuna-databricks-on-aws.md

# Citations

1. [hyperparameter-tuning-with-optuna-databricks-on-aws.md](/references/hyperparameter-tuning-with-optuna-databricks-on-aws-acf7dc85.md)
