One-Hot Encoding

参考：
What is One Hot Encoding? Why And When do you have to use it?
preprocessing categorical features

1. 一组数据经过 One-Hot Encoding 处理后的结果，可以清楚的看出One-Hot Encoding 具体的做的事情。

One-Hot Encoding 处理前
One-Hot Encoding 处理后

这个过程可以用这句话概括：

This estimator transforms each categorical feature with m possible values into m binary features, with only one active.

2. 为什么需要 One-Hot Encoding

对于类别，在向量化的时候会编码成数字，由于类别之间没有明确的数值关系，编码产生的数字，会默认给类编加上数值关系，如下所述：

Let me explain: What this form of organization presupposes is VW > Acura > Honda based on the categorical values. Say supposing your model internally calculates average, then accordingly we get, 1+3 = 4/2 =2. This implies that: Average of VW and Honda is Acura. This is definitely a recipe for disaster. This model’s prediction would have a lot of errors.

One-Hot Encoding 实际将类别信息二进制化， 如果属于相应类别，相应值为 1， 否则为 0， 这样避在编码类别时，引入无关的数值关系。