Scikit-learn's LabelBinarizer vs. OneHotEncoder

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 arrayfrom sklearn.preprocessing import LabelEncoderfrom sklearn.preprocessing import OneHotEncoderfrom sklearn.preprocessing import LabelBinarizer# define exampledata = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']values = array(data)print "Data: ", values# integer encodelabel_encoder = LabelEncoder()integer_encoded = label_encoder.fit_transform(values)print "Label Encoder:" ,integer_encoded# onehot encodeonehot_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 encodelb = 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.

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

from sklearn.preprocessing import LabelBinarizer, LabelEncoder, OneHotEncoderX = [["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 binarizationLabelEncoder().fit_transform(X)# ValueError: bad input shape (3, 2)

Scikitlearn suggests using OneHotEncoder for X matrix i.e. the features you feed in a model, and to use a LabelBinarizer for the y labels.

They are quite similar, except that OneHotEncoder could return a sparse matrix that saves a lot of memory and you won't really need that in y labels.

Even if you have a multi-label multi-class problem, you can use MultiLabelBinarizer for your y labels rather than switching to OneHotEncoder for multi hot encoding.

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html