missmixed 1.0.0

A modular framework for missing value imputation...

MissMixed

A Configurable Framework for Iterative Missing Data Imputation

MissMixed is a Python library designed for flexible and modular imputation of missing values in tabular datasets. It supports a wide range of imputation strategies, including ensemble methods, trial-based model selection, and deep learning integration — all within a customizable iterative architecture.

🔍 What is MissMixed?

MissMixed is not just a single algorithm — it’s a framework for building iteration-wise, model-aware imputation pipelines. It enables users to:

• Handle continuous, categorical, or mixed-type features
• Define custom model configurations at each iteration
• Combine multiple imputation algorithms (e.g., RandomForest, KNN, Deep Neural Networks)
• Dynamically evaluate and update imputed values using internal validation

Whether you’re working with low-dimensional medical data or large-scale mixed-type datasets, MissMixed is designed to offer accuracy, adaptability, and interpretability.

🚀 Installation

pip install missmixed

📦 Requirements

• Python ≥ 3.9
• NumPy
• Pandas
• scikit-learn
• XGBoost
• TensorFlow or Keras (for deep model imputation)
• tqdm

Dependencies will be installed automatically via pip.

📖 Usage

See the examples folder for how to define: Custom Iteration Architectures Mixed-type pipelines Trial-based imputation workflows

📄 License

MIT License

📣 Citation

[1] M. M. Kalhori, M. Izadi, “A Novel Mixed-Method Approach to Missing Value Imputation: An Introduction to MissMixed”, 29th International Computer Conference, Computer Society of Iran (CSICC) – IEEE, 2025.

[2] M. M. Kalhori, M. Izadi, F. Akbari “MissMixed: An Adaptive, Extensible and Configurable Multi-Layer Framework for Iterative Missing Value Imputation”, IEEE Access, 2025 (under review).