mlguardlabs 0.1.0

Machine learning experiment manager and leakage detection toolkit

MLGuard

MLGuard is a lightweight machine learning experiment management and data validation toolkit designed to help detect potential data leakage and benchmark multiple models quickly.

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🚀 Features

• 🔍 Data Leakage Detection Detects potential target leakage by analyzing correlations between features and the target.

• ⚙️ Experiment Manager Automatically runs multiple ML models and evaluates them.

• 📊 Model Comparison Compare model performance using appropriate metrics.

• 🧠 Automatic Problem Detection Detects whether the task is regression or classification.

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📦 Installation

pip install mlguard

or install locally:

pip install -e .

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⚡ Quick Example

Regression Example

from sklearn.datasets import fetch_california_housing
from mlguard.experiments import ExperimentManager

data = fetch_california_housing(as_frame=True)

X = data.data
y = data.target

exp = ExperimentManager()

exp.fit(X, y)

print(exp.compare())

Example output:

Running leakage detection...
No leakage detected

Detected problem type: regression

[('rf', 0.50), ('ridge', 0.74)]

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Classification Example

from sklearn.datasets import load_iris
from mlguard.experiments import ExperimentManager

data = load_iris(as_frame=True)

X = data.data
y = data.target

exp = ExperimentManager()

exp.fit(X, y)

print(exp.compare())

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🏗 Project Structure

mlguard/
│
├── experiments
│   └── experiment_manager.py
│
├── inspection
│   └── leakage_detector.py
│
├── metrics
│   ├── regression_metrics.py
│   └── classification_metrics.py
│
├── utils
│   └── problem_type.py
│
├── examples
├── tests

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🧠 How MLGuard Works

MLGuard runs a lightweight validation and experimentation pipeline:

Dataset
   ↓
Leakage Detection
   ↓
Problem Type Detection
   ↓
Model Experiments
   ↓
Model Evaluation
   ↓
Model Comparison

This helps ML practitioners quickly answer:

Which model works best for my dataset?

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🛣 Roadmap

Planned improvements:

• Cross-validation experiment engine
• Dataset inspector
• Automatic preprocessing pipelines
• Feature validation tools
• Experiment tracking

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch
3. Submit a pull request

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📜 License

MIT License © 2026 Tarun