Hi Jeremy! Thanks for your offer to contribute. We're always looking for people to add good ideas to the package. Time series data can be tricky to handle appropriately, and so I think we generally try to pass it off to more specialized packages that focus on that. Andreas may have a more detailed perspective on this though.
Jacob On Thu, Jul 6, 2017 at 12:50 PM, jt cunni <jtcu...@gmail.com> wrote: > First off, I have never contributed to anything before so please have > patience with me. I am a data scientist and I have been working with doing > some feature engineering on one of my datasets. In my code, I have a > pipeline of several transformers and an estimator. I use my pipeline > and randomizedsearchcv to tune my hyper-parameters and my transformer > settings. Pretty standard stuff. One thing I was doing was creating a > feature that was a moving average of another feature. In a basic example, > imagine I want to predict if a team is going to win a baseball game. I > create a feature that is the moving average of the last N games of runs > scored per game (this is the window size of the moving average). Not > knowing what the best window size for the moving average, I created a > custom transformer that could be put in a pipeline to find the window size > that provides the most lift. Is there any interest for this type of > contribution? If so, what unittests or anything else do I need to provide? > > > > Thanks, > > Jeremy > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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