Python machine learning library based on Object Oriented design principles; the goal is to allow users to quickly explore data and search for top machine learning algorithm candidates for a given dataset
MIT License
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modify ModelFitter to *optionally* take a Splitter, and train on all data #3
A common use case, which isn't supported, is that a ModelFitter should train on ALL the data.
So, if a Splitter is not passed in, there will be no split, the transformations will be fit on all the data, the model will be trained on all the data, and there will not be a corresponding holdout_evaluator or holdout_scores (they will be `None)
A common use case, which isn't supported, is that a
ModelFittershould train on ALL thedata.So, if a
Splitteris not passed in, there will be no split, the transformations will be fit on all the data, the model will be trained on all the data, and there will not be a correspondingholdout_evaluatororholdout_scores(they will be `None)