mlarena 0.2.1

An algorithm-agnostic machine learning toolkit for model training, diagnostics and optimization

MLArena

An algorithm-agnostic machine learning toolkit for model training, diagnostics and optimization.

Publications

Read about the concepts and methodologies behind MLArena through these articles:

1. Algorithm-Agnostic Model Building with MLflow - Published in Towards Data Science

   A foundational guide demonstrating how to build algorithm-agnostic ML pipelines using mlflow.pyfunc. The article explores creating generic model wrappers, encapsulating preprocessing logic, and leveraging MLflow's unified model representation for seamless algorithm transitions.

2. Explainable Generic ML Pipeline with MLflow - Published in Towards Data Science

   An advanced implementation guide that extends the generic ML pipeline with more sophisticated preprocessing and SHAP-based model explanations. The article demonstrates how to build a production-ready pipeline that supports both classification and regression tasks, handles feature preprocessing, and provides interpretable model insights while maintaining algorithm agnosticism.

Installation

The package is undergoing rapid development at the moment (pls see CHANGELOG for details), it is therefore highly recommended to install with specific versions. For example

%pip install mlarena==0.2.0

If you are using the package in Databricks ML Cluster with DBR runtime >= 16.0, you can install without dependencies like below:

%pip install mlarena==0.2.0 --no-deps

If you are using earlier DBR runtimes, simply install optuna in addition like below. Note: As of 2025-04-26, optuna is recommended by Databricks, while hyperopt will be removed from Databricks ML Runtime.

%pip install mlarena==0.2.0 --no-deps
%pip install optuna==3.6.1

Usage Example

• For quick start with a basic example, see 1.basic_usage.ipynb.
• For more advanced examples on model optimization, see 2.advanced_usage.ipynb.
• For visualization utilities, see 3.utils_plot.ipynb.
• For handling common challenges in machine learning, see 4.ml_discussions.ipynb.

Visual Examples:

Model Performance Analysis

Explainable ML

One liner to create global and local explanation based on SHAP that will work across various classification and regression algorithms.

Hyperparameter Optimization

Parallel coordinates plot for hyperparameter search space diagnostics.

Features

Algorithm Agnostic ML Pipeline

• Unified interface for any scikit-learn compatible model
• Consistent workflow across classification and regression tasks
• Automated report generation with comprehensive metrics and visuals
• Production-ready with MLflow integration for deployment
• Simplified handoff between experimentation and production

Intelligent Preprocessing

• Streamlined feature preprocessing with smart defaults and minimal code
• Automatic feature analysis with data-driven encoding recommendations
• Integrated target encoding with visualization for optimal smoothing selection
• Feature filtering based on information theory metrics (mutual information)
• Handles the full preprocessing pipeline from missing values to feature encoding
• Seamless integration with scikit-learn and MLflow for production deployment

Model Optimization

• Efficient hyperparameter tuning with Optuna's TPE sampler
• Smart early stopping with patient pruning to save computation resources
• Cross-validation with variance penalty to prevent overfitting
• Parallel coordinates visualization for search history tracking and parameter space diagnostics
• Automated threshold optimization with business-focused F-beta scoring
• Flexible metric selection for optimization
  • Classification: AUC (default), F1, accuracy
  • Regression: RMSE (default), NRMSE, MAPE

Performance Analysis

• Comprehensive metric tracking
  • Classification: AUC, F1, Fbeta, precision, recall
  • Regression: RMSE, MAE, R2, adjusted R2, MAPE
• Performance visualization
  • Classification: ROC_AUC curve, Precision-recall curve
  • Regression: Residual analysis, Prediction error plot
• Model interpretability
  • Global feature importance
  • Local prediction explanations

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.