Hi, Georg,

I bring this up every time here on the mailing list :), and you probably aware 
of this issue, but it makes a difference whether your categorical data is 
nominal or ordinal. For instance if you have an ordinal variable like with 
values like {small, medium, large} you probably want to encode it as {1, 2, 3} 
or {1, 20, 100} or whatever is appropriate based on your domain knowledge 
regarding the variable. If you have sth like {blue, red, green} it may make 
more sense to do a one-hot encoding so that the classifier doesn't assume  a 
relationship between the variables like blue > red > green or sth like that.

Now, the DictVectorizer and OneHotEncoder are both doing one hot encoding. The 
LabelEncoder does convert a variable to integer values, but if you have sth 
like {small, medium, large}, it wouldn't know the order (if that's an ordinal 
variable) and it would just assign arbitrary integers in increasing order. 
Thus, if you are dealing ordinal variables, there's no way around doing this 
manually; for example you could create mapping dictionaries for that (most 
conveniently done in pandas).


> On Aug 5, 2017, at 5:10 AM, Georg Heiler <georg.kf.hei...@gmail.com> wrote:
> Hi,
> the LabelEncooder is only meant for a single column i.e. target variable. Is 
> the DictVectorizeer or a manual chaining of multiple LabelEncoders (one per 
> categorical column) the desired way to get values which can be fed into a 
> subsequent classifier?
> Is there some way I have overlooked which works better and possibly also can 
> handle unseen values by applying most frequent imputation?
> regards,
> Georg
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