What Kagglers Are Using for Text Classification

Re:What Kagglers Are Using for Text Classification by soraki_soladead on Friday December 28, 2018 @12:15AM (#18775020)

I agree good models like xgboost get buried and inexperienced practitioners jump to deep models too quickly. Often without understanding how to properly architect and tune them. Always start with a simple baseline (EDIT: and good process).

However, what's lacking in the ML practitioner community is nuance. Some applications need deep models some problems need xgboost. There isn't a "best" model in text classification because it depends on your data and problem.

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Re:What Kagglers Are Using for Text Classification by asah on Friday December 28, 2018 @05:59AM (#18776104)

This chart is great for newbies, perhaps give it an update?

https://scikit-learn.org/stable/tutorial/machine_learning_ma...

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Re:What Kagglers Are Using for Text Classification by autokad on Friday December 28, 2018 @10:59AM (#18777463)

I'm not a huge fan of the chart as it looks pretty 2014 dated. SVMs are rarely used anymore, so perhaps if you replaced all of them (including in the regression part) with gbm. Didn't notice any mention of logistic regression, which I still heavily use as well.

dimentionality reduction is part of any step, which depends on number of observations, features, and model. my favorite is l1 or pca, but I am not afraid to use stepwise regression or some tree method.

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