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https://forums.fast.ai/t/to-label-encode-or-one-hot-encode/6057

To Label Encode or One Hot Encode?

When is it appropriate to use label encoding vs. one hot encoding and vice-versa? What got me thinking about this was working through the Kaggle Titatnic dataset and taking note of how most folks were handling the "Sex"…



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To Label Encode or One Hot Encode?

https://forums.fast.ai/t/to-label-encode-or-one-hot-encode/6057

When is it appropriate to use label encoding vs. one hot encoding and vice-versa? What got me thinking about this was working through the Kaggle Titatnic dataset and taking note of how most folks were handling the "Sex"…



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https://forums.fast.ai/t/to-label-encode-or-one-hot-encode/6057

To Label Encode or One Hot Encode?

When is it appropriate to use label encoding vs. one hot encoding and vice-versa? What got me thinking about this was working through the Kaggle Titatnic dataset and taking note of how most folks were handling the "Sex"…

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      When is it appropriate to use label encoding vs. one hot encoding and vice-versa? What got me thinking about this was working through the Kaggle Titatnic dataset and taking note of how most folks were handling the "Sex"…
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      When is it appropriate to use label encoding vs. one hot encoding and vice-versa? What got me thinking about this was working through the Kaggle Titatnic dataset and taking note of how most folks were handling the “Sex” column, which has no missing values and is either “Male” or “Female”. Almost everyone simply uses label encoding for this feature, and that encoding is always Male=1, Female=0. BUT, my understanding of label encoding is it only makes sense when they represent “a natural ordere...

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