Christoph Molnar
@christophmolnar.bsky.social did:plc:l3zkaaeogtmigemy7oi45sot
Author of Interpretable Machine Learning and other books Newsletter: https://mindfulmodeler.substack.com/ Website: https://christophmolnar.com/
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Christoph Molnar (@christophmolnar.bsky.social) es un usuario de Bluesky que se unió el 12 noviembre 2024. Tiene 6.274 seguidores, sigue a 966 cuentas y ha publicado 143 posts.
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Sounds interesting? Have a look at our paper! Joint work with Eric Günther and @ulrikeluxburg.bsky.social.
Ver postDIP is ✅ unique under mild assumptions, ✅ easy to interpret, ✅ entails an efficient estimation procedure, ✅ describes properties of the data (instead of just a specific model), and ✅ comes with a python implementation (github.com/gcskoenig/dipd).
Ver postDependencies are not only a neglected cooperative force but also complicate the definition and quantification of feature interactions. In particular, the contributions of interactions and dependencies may cancel each other out and must be disentangled to be fully revealed.
Ver postFor example, suppose we predict kidney function (Y) from creatinine (C) and muscle mass (M), and that C reflects Y but also M, which is not linked to Y. Here, M becomes useful once combined with C, as it allows us to subtract irrelevant variation from C. In other words, C&M cooperate via dependence!
Ver postDetermining whether variables are relevant due to cooperation is crucial, as variables that cooperate must be considered jointly to understand their relevance. Notably, features cooperate not only through interactions but also through statistical dependencies, which existing methods neglect.
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