Scikit-learn's LabelBinarizer vs. OneHotEncoder

Question

What is the difference between the two? It seems that both create new columns, which their number is equal to the number of unique categories in the feature. Then they assign 0 and 1 to data points depending on what category they are in.

Answer 1

A simple example which encodes an array using LabelEncoder, OneHotEncoder, LabelBinarizer is shown below.

I see that OneHotEncoder needs data in integer encoded form first to convert into its respective encoding which is not required in the case of LabelBinarizer.

from numpy import array from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import OneHotEncoder from sklearn.preprocessing import LabelBinarizer  # define example data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold',  'warm', 'hot'] values = array(data) print "Data: ", values # integer encode label_encoder = LabelEncoder() integer_encoded = label_encoder.fit_transform(values) print "Label Encoder:" ,integer_encoded  # onehot encode onehot_encoder = OneHotEncoder(sparse=False) integer_encoded = integer_encoded.reshape(len(integer_encoded), 1) onehot_encoded = onehot_encoder.fit_transform(integer_encoded) print "OneHot Encoder:", onehot_encoded  #Binary encode lb = LabelBinarizer() print "Label Binarizer:", lb.fit_transform(values) 

Another good link which explains the OneHotEncoder is: Explain onehotencoder using python

There may be other valid differences between the two which experts can probably explain.

Answer 2

A difference is that you can use OneHotEncoder for multi column data, while not for LabelBinarizer and LabelEncoder.

from sklearn.preprocessing import LabelBinarizer, LabelEncoder, OneHotEncoder  X = [["US", "M"], ["UK", "M"], ["FR", "F"]] OneHotEncoder().fit_transform(X).toarray()  # array([[0., 0., 1., 0., 1.], #        [0., 1., 0., 0., 1.], #        [1., 0., 0., 1., 0.]]) 

LabelBinarizer().fit_transform(X) # ValueError: Multioutput target data is not supported with label binarization  LabelEncoder().fit_transform(X) # ValueError: bad input shape (3, 2)