ML Model Benchmarking

A reproducible machine learning benchmarking framework for comparing classification models across public datasets using standardized evaluation metrics.

This repository demonstrates practical machine learning engineering workflows including:

• model benchmarking
• cross-validation evaluation
• reproducible experimentation
• modular training pipelines

The project is designed as a demonstration of machine learning engineering patterns using public datasets. Proprietary algorithms and production systems are not included.

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Models Included

• Logistic Regression
• Random Forest
• Support Vector Machine

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Repository Structure

src/
        models.py
        data_loader.py
        evaluate.py
        benchmark.py
    
experiments/
        benchmark_config.yaml

results/

Quick Start

Install dependencies:

pip install -r requirements.txt

Run the benchmark:

python src/benchmark.py

Results will be saved to:

results/benchmark_results.csv

Generate a comparison plot:

python src/plot_results.py

The visualization will be saved to:

results/benchmark_plot.png

Components

models.py Defines the machine learning models used in benchmarking experiments.

data_loader.py Loads datasets used for benchmarking.

evaluate.py Provides utilities for evaluating model performance using cross-validation.

benchmark.py Runs the full benchmarking pipeline.

Example Output

logistic_regression: 0.9732
random_forest: 0.9651
svm: 0.9714

Notes

This repository contains demonstration implementations designed to illustrate machine learning engineering patterns using public datasets. Proprietary algorithms and production systems are not included.