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Pluviophile
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Personally I would chose one hot encoding as it is statistically correct/model friendly. Moreover, you can always provide additional help/tools to aid explainability. Lastly even if you add the nth column, you still need some idea about the working of model(and the boundaries it created while training) to interpret the result.

My personal opinion ~Abhinav

Personally I would chose one hot encoding as it is statistically correct/model friendly. Moreover, you can always provide additional help/tools to aid explainability. Lastly even if you add the nth column, you still need some idea about the working of model(and the boundaries it created while training) to interpret the result.

My personal opinion ~Abhinav

Personally I would chose one hot encoding as it is statistically correct/model friendly. Moreover, you can always provide additional help/tools to aid explainability. Lastly even if you add the nth column, you still need some idea about the working of model(and the boundaries it created while training) to interpret the result.

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Personally I would chose one hot encoding as it is statistically correct/model friendly. Moreover, you can always provide additional help/tools to aid explainability. Lastly even if you add the nth column, you still need some idea about the working of model(and the boundaries it created while training) to interpret the result.

My personal opinion ~Abhinav