---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 9cff6e7d0161ceabb1ecf741298698573b3222b7f2d8d40ac386464e48c0fb79
  pageDirectory: concepts
  sources:
    - compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-model-comparison
    - MMC
  citations:
    - file: compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md
title: MLflow Model Comparison
description: Using MLflow to track, compare, and evaluate performance across multiple model types and hyperparameter configurations during model selection.
tags:
  - machine-learning
  - mlflow
  - experiment-tracking
timestamp: "2026-06-18T11:03:50.436Z"
---

# MLflow Model Comparison

**MLflow Model Comparison** is a methodology for systematically evaluating and selecting the best machine learning model among multiple candidate model types by leveraging [MLflow Tracking](/concepts/mlflow-tracking.md) and [Hyperopt](/concepts/hyperopt.md) for hyperparameter optimization. This approach uses MLflow to log and compare the performance of different model architectures, each tuned with their own set of appropriate hyperparameters, to arrive at a single best-performing model overall.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

## Overview

When building a machine learning solution, data scientists often need to choose between several model types — for example, a random forest, a gradient-boosted tree, or a linear regression — before committing to a final architecture. Comparing models by training each with default parameters is not sufficient, because different model types have different sets of hyperparameters that influence their performance. A rigorous comparison requires tuning each model type with a hyperparameter search space appropriate to that model's architecture, then comparing the best-found configurations across model types.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

MLflow Model Comparison provides a structured workflow for this task. It combines [Hyperopt](/concepts/hyperopt.md) (or a compatible alternative like [Optuna](/concepts/optuna.md) or [RayTune](/concepts/raytune.md)) for distributed hyperparameter search with MLflow for experiment tracking, result logging, and model registry.

## Workflow

The typical workflow for comparing model types with Hyperopt and MLflow is:

1. **Define a search space** for each model type containing the hyperparameters relevant to that architecture. A random forest, for example, tunes `n_estimators` and `max_depth`, while a gradient-boosted tree may tune `learning_rate` and `subsample`.
2. **Run a hyperparameter tuning sweep** for each model type using Hyperopt (with `SparkTrials` or `Trials`). Each trial logs its configuration and metrics to an MLflow experiment run.
3. **Compare the best runs** across model types using MLflow's experiment comparison UI or programmatic APIs, inspecting validation metrics such as accuracy, log loss, or RMSE.
4. **Select the winning model** — the one that achieves the best objective metric — and promote it to [MLflow Model Registry](/concepts/mlflow-model-registry.md) for production deployment.

The key principle is that each model type is given its own fair set of hyperparameter choices. Without this, a model type with many tunable knobs might be unfairly penalized if only a single set of defaults is compared, or might be overfit if too many trials are run.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

## Tools Used

### Hyperopt

[Hyperopt](/concepts/hyperopt.md) is a Python library for hyperparameter optimization that uses algorithms such as [Tree of Parzen Estimators (TPE)](/concepts/hyperopt-tree-of-parzen-estimators-tpe-algorithm.md) to explore search spaces efficiently. On Databricks, Hyperopt can be combined with `SparkTrials` to distribute the search across a Spark cluster, enabling large-scale sweeps that would be impractical on a single machine.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

### MLflow

[MLflow](/concepts/mlflow.md) is the platform that records every trial's parameters, metrics, and artifacts. The MLflow UI provides a centralized view to compare runs from different model types side by side, making it possible to identify which model type, at its best configuration, outperforms the others.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

## Comparison Strategy

The comparison proceeds in two phases:

- **Phase 1: Per-model-type tuning**. For each candidate model type (e.g., `sklearn.ensemble.RandomForestClassifier`, `sklearn.ensemble.GradientBoostingClassifier`, `sklearn.svm.SVC`), define a `hyperopt` search space of the hyperparameters specific to that model. Run a Hyperopt sweep (e.g., 50–100 trials) for each type.
- **Phase 2: Cross-type comparison**. MLflow groups all runs under a single experiment. Use the MLflow UI's **compare** feature or the `mlflow.search_runs()` API to compare the best trial of each model type. The model with the highest validation metric (or lowest loss, depending on the objective) is the overall winner.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

## Example: scikit-learn Classifier Comparison

A common application is comparing three scikit-learn classifiers on a binary classification task:

| Model Type | Tuned Hyperparameters |
|---|---|
| `RandomForestClassifier` | `n_estimators`, `max_depth`, `min_samples_split` |
| `GradientBoostingClassifier` | `n_estimators`, `learning_rate`, `max_depth`, `subsample` |
| `SVC` | `C`, `kernel`, `gamma` |

Each model type's Hyperopt search space is tailored to its own knobs. After running sweeps, MLflow's best-run comparison shows that, for example, `GradientBoostingClassifier` with `learning_rate=0.05` and `n_estimators=200` achieves a validation accuracy of 0.94, outperforming the best `RandomForest` (0.91) and `SVC` (0.88). That boosted tree becomes the selected model for deployment.^[compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md]

## Related Concepts

- [Hyperparam tuning](/concepts/hyperparameter-tuning.md) — Search for optimal hyperparameter values
- [Model selection](/concepts/custom-judge-model-selection.md) — The process of choosing among candidate models
- Cross-validation — Technique used inside each trial to estimate generalization
- [MLflow Tracking](/concepts/mlflow-tracking.md) — Logging system for ML experiments and results
- [MLflow Model Registry](/concepts/mlflow-model-registry.md) — Versioned model storage for production deployment
- [Optuna](/concepts/optuna.md) — An alternative single-node hyperparameter optimization library
- [RayTune](/concepts/raytune.md) — A distributed alternative for hyperparameter tuning

## Sources

- compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md

# Citations

1. [compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws.md](/references/compare-model-types-with-hyperopt-and-mlflow-databricks-on-aws-24ee58b3.md)
