from sklearn.preprocessing import LabelEncoder
l_labels = ['[PAD]'] ['NN', 'ADJ', 'PRON']
le = LabelEncoder()
le.fit(l_labels)
le.trasform('[PAD]')
>>>> 3
I want the encodind of '[PAD]' to be 0. Is it possible to bind a label to an encoding with LabelEncoder ?
CodePudding user response：
the scikit learn LabelEncoder is sorting the list of element before the transformation one way to encode 'PAD' to be 0 is the change the name of PAD to some thing that will be sorted as first.
l_labels = ['0' 'PAD'] ['NN', 'ADJ', 'PRON']
le = LabelEncoder()
le.fit(l_labels)
le.transform(['0' 'PAD'])
>> [0]
CodePudding user response：
No, you cannot do that in LabelEncoder because it first finds the unique elements and then sorts them to assign numerical encoding.
what happens internally in the fit method.
uniques_set = set(values)
uniques_set, missing_values = _extract_missing(uniques_set)
uniques = sorted(uniques_set)