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  Try out Haskell dataframe pre-loaded into a jupyter notebook: https://ihaskell-dataframe-crf7g5fvcpahdegz.westus2-01.azurewebsites.net/lab

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  Starting a series on programming synthesis https://mchav.github.io/an-introduction-to-program-synthesis/

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  Have some pretty cool examples of feature engineering using program synthesis on Haskell data frames. Given a function space, we run a breadth first search to find what functions (and their compositions) have the highest correlation with a target variable. https://github.com/mchav/dataframe/blob/feature_engineering/app/Main.hs#L32

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  Wrote a new article where I checkpoint the work we’ve done so far enabling Kaggle style EDA-to-model workflows in Haskell. https://mchav.github.io/iris-classification-in-haskell/

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  @jonocarroll would appreciate if you joined as an advisor or tastemaker of sorts. https://datahaskell.org/blog/2025/11/11/welcome-to-datahaskell.html

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  I’ve been thinking a lot about how to scale symbolic regression: this seems like a plausible direction. https://mchav.github.io/grow-and-mow/