Difference between OrdinalEncoder and LabelEncoder I was going through the official documentation of scikit-learn learn after going through a book on ML and came across the following thing:

In the Documentation it is given about sklearn.preprocessing.OrdinalEncoder() whereas in the book it was given about sklearn.preprocessing.LabelEncoder(), when I checked their functionality it looked same to me. Can Someone please tell me the difference between the two please?
Afaik, both have the same functionality. A bit difference is the idea behind. OrdinalEncoder is for converting features, while LabelEncoder is for converting target variable.

That's why OrdinalEncoder can fit data that has the shape of (n_samples, n_features) while LabelEncoder can only fit data that has the shape of (n_samples,) (though in the past one used LabelEncoder within the loop to handle what has been becoming the job of OrdinalEncoder now)