milearn让多实例学习像scikit-learn一样简单，支持经典与神经网络方法。
milearn: A Python Package for Multi-Instance Machine Learning
- 封装经典与神经网络多实例学习算法，兼容scikit-learn接口。
- 内置针对小数据集的超参数优化，提升数据稀缺下的模型选择能力。
- 支持关键实例检测（KID）等任务，适用于分子、蛋白等复杂预测场景。
我们介绍milearn，一个遵循scikit-learn风格的多实例学习（MIL）Python包，统一支持分类与回归任务中的经典及基于神经网络的MIL算法。该包专为小规模MIL数据集设计了内置超参数优化，增强数据稀缺场景下的模型选择鲁棒性。我们在多种合成基准数据集上验证其通用性，涵盖数字分类与回归、分子性质预测及蛋白质-蛋白质相互作用（PPI）预测。特别强调关键实例检测（KID）问题，提供专门支持。
原文摘要 · Abstract (English)
We introduce milearn, a Python package for multi-instance learning (MIL) that follows the familiar scikit-learn fit/predict interface while providing a unified framework for both classical and neural-network-based MIL algorithms for regression and classification. The package also includes built-in hyperparameter optimization designed specifically for small MIL datasets, enabling robust model selection in data-scarce scenarios. We demonstrate the versatility of milearn across a broad range of synthetic MIL benchmark datasets, including digit classification and regression, molecular property prediction, and protein-protein interaction (PPI) prediction. Special emphasis is placed on the key instance detection (KID) problem, for which the package provides dedicated support.
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