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ML topics that can confuse new people #5

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ianozsvald opened this issue Apr 25, 2016 · 0 comments

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ianozsvald commented Apr 25, 2016 •

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handling missing data (imputation), dropping these rows, 'fixing them', changing the distribution without realising it (can I visualise this as a demo?)
class imbalance - massive imbalances (e.g. click data) mean that accuracy isn't so useful
lack of visualisation and pair-plots, so there's no gut feel for the underlying data
how to layout files: https://drivendata.github.io/cookiecutter-data-science/

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