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Towards the end, when we're using OneHotEncoder, the key shows
fireplace_qu_encoded_train = ohe.transform(fireplace_qu_train)
and then a printout of a numpy array (instead of the sparse matrix object that is returned). Run this way, there is an error in the next code block because of mismatched shapes.
I added .toarray() on the above code. (Not sure if that's the best way...)
Could we add ColumnTransformer
to this lab?
If so, we'd need to have introduced it earlier, perhaps in the OOP lectures. We could potentially ask that students, after building their scalers, test them by putting them into a ColumnTransformer.
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