why One-Hot Encoder can avoid the situation that the model will misunderstand the data to be in some kind of order if the data has been Label Encoding We know that we prefer to using One-Hot Encoding not Label Encoding when processing with non-ordinal data. 
And I real a blog which give the difference between Label Encoding and One-Hot Encoding.
So I am wondering why One-Hot Encoder can avoid the situation that the model will misunderstand the data to be in some kind of order, $0 &lt; 1 &lt; 2$ if the data has been Label Encoding.
Does it have some provements and theories?
Or anyone just can explain it in intuition.
Thanks in advance.
From the blog, one hot encoding resolves the issue by explicitly showing that 1 category is true, while all others are false (1 vs 0). One column is turned into 3 columns which describe all categories as either true or false. 

As opposed to letting a model see one column with 0, 1, and 2. One column to show this data with label encoding does make it seem as if the data is numerical so 0 &lt; 1 &lt; 2.