Functional Machine Learning Framework // A compact and explicit machine learning framework for supervised learning, resampling- based evaluation, hyperparameter tuning, learner comparison, interpretation, and plug-in g-computation. The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) doi:10.1214/aos/1013203451, Goldstein et al. (2015) doi:10.1080/10618600.2014.907095, Apley and Zhu (2020) doi:10.1111/rssb.12377, Lundberg and Lee (2017) doi:10.48550/arXiv.1705.07874, and Ribeiro et al. (2016) doi:10.48550/arXiv.1602.04938. The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems.