{"doi":"10.21105/joss.06469","title":"Contextualized: Heterogeneous Modeling Toolbox","abstract":"Heterogeneous and context-dependent systems are common in real-world processes, such as those in biology, medicine, finance, and the social sciences.However, learning accurate and interpretable models of these heterogeneous systems remains an unsolved problem.Most statistical modeling approaches make strict assumptions about data homogeneity, leading to inaccurate models, while more flexible approaches are often too complex to interpret directly.Fundamentally, existing modeling tools force users to choose between accuracy and interpretability.Recent work on Contextualized Machine Learning (Lengerich et al., 2023) has introduced a new paradigm for modeling heterogeneous and context-dependent systems, which uses contextual metadata to generate sample-specific models, providing context-specific model-based insights and representing data heterogeneity with context-dependent model parameters.Here, we present Contextualized, a SKLearn-style Python package for estimating and analyzing personalized context-dependent models based on Contextualized Machine Learning.Contextualized implements two reusable and extensible concepts: a context encoder which translates sample context or metadata into model parameters, and sample-specific model which is defined by the context-specific parameters.With the flexibility of context-dependent parameters, each context-specific model can be a simple model class, such as a linear or Gaussian model, providing direct model-based interpretability without sacrificing overall accuracy.","journal":"Journal of Open Source Software","year":2024,"id":2953,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0492,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-05-08","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":18778,"name":"Benjamin J. Lengerich","orcid":"0000-0001-8690-9554","position":1,"is_corresponding":false},{"id":32753,"name":"Wesley Lo","orcid":null,"position":2,"is_corresponding":false},{"id":32754,"name":"Aaron Alvarez","orcid":null,"position":3,"is_corresponding":false},{"id":32755,"name":"Andrea Rubbi","orcid":null,"position":4,"is_corresponding":false},{"id":14693,"name":"Sharon L. R. Kardia","orcid":"0000-0002-9853-3379","position":5,"is_corresponding":false},{"id":18781,"name":"Eric P. Xing","orcid":"0009-0005-9158-4201","position":6,"is_corresponding":false},{"id":32756,"name":"W. Amber Lo","orcid":null,"position":7,"is_corresponding":false},{"id":32757,"name":"A. Gonzalez Alvarez","orcid":null,"position":8,"is_corresponding":false},{"id":18779,"name":"Caleb N. Ellington","orcid":"0000-0001-7029-8023","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}