One-hot-encoding with missing categories
Basically first we need to apply fit_transform for the base data and next apply transform for the sample data, so sample data also will get the exact no.of columns w.r.t base data.
I have a dataset with a category column. In order to use linear regression, I 1-hot encode this column. 
My set has 10 columns, including the category column. After dropping that column and appending the 1-hot encoded matrix, I end up with 14 columns (10 - 1 + 5).
So I train (fit) my LinearRegression model with a matrix of shape (n, 14).
After training it, I want to test it on a subset of the training set, so I take only the 5 first and put them through the same pipeline. But these 5 first only contain 3 of the categories. So after going through the pipeline, I'm only left with a matrix of shape (n, 13) because it's missing 2 categories. 
How can I force the 1-hot encoder to use the 5 categories ?
I'm using LabelBinarizer from sklearn.