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future ideas: #151
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once trained, ask users if they'd be ok sharing some summary info about their data and the parameters of their models (totally anonymized) with us
we could then figure out what combinations of params work best for data of a certain shape (num features, standard dev, min, max, range, num data points, type of predicted output, num of categories being predicted, etc.).
offer a "Quickstart" option- default to whatever we predict will be the best params for your data set, and train algos with those- don't traverse the param space trying all combos of params.
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