Merci, ça fait plaisir. (Je devrais passer plus souvent ici)
Big thinkers of urban planning, who designed spaces and cities accounting for interactions between builders, transit, roads... connected their thinking with sociology and related. People thinking software at the ecosystem level probably should do the same.
👋 Thanks for doing this! Would you please add me! 🙏
Mais ce soir, si vous êtes francophone, il y a plus important: le naufrage inquiétant de notre démocratie qui se doit de réunir et de construire bsky.app/profile/gael...
First, we have much more opinionated choices here, while scikit-learn is consensus in ML (aka, textbook). Second, we make brutal use of dataframes everywhere, while scikit-learn has only optional dependence on them.
Skrub is not really designed for signals, rather databases of relational data, with dates, strings, multiple tables to join. Check out MNE-python for EEG.
Join us: this is open source, and the power of such a project is the ability to build in common. Let's create together a much-needed tool for data science github.com/skrub-data/s... 8/8
Prepping tables for machine learning. Contribute to skrub-data/skrub development by creating an account on GitHub.
Skrub is very young, and there is a lot more that needs done. For instance, we want to support multiple dataframe backends and lazy modes. Our dream is to streamline developing and put in production machine-learning by coupling the scikit_learn API to database operations. 7/8
Each functionality comes as a scikit_learn transformer: Joiner (skrub-data.org/stable/gener...skrub-data.org/stable/gener...) Separate "fit" and "transform" avoid prediction-time problems. They enable hyper-parameter tuning (eg adding a "day of the week column") 6/8
