Scikit-learn 的 LabelBinarizer 与 OneHotEncoder [英] Scikit-learn's LabelBinarizer vs. OneHotEncoder

问题描述

两者有什么区别?似乎两者都创建了新列，它们的数量等于特征中唯一类别的数量.然后他们根据数据点所在的类别将 0 和 1 分配给数据点.

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.

推荐答案

一个使用 LabelEncoder、OneHotEncoder、LabelBinarizer 对数组进行编码的简单示例如下所示.

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

我看到 OneHotEncoder 首先需要整数编码形式的数据才能转换为其各自的编码，而在 LabelBinarizer 的情况下不需要.

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)

另一个解释 OneHotEncoder 的好链接是:使用 python 解释 onehotencoder

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

这两者之间可能存在其他专家可以解释的有效差异.

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

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