What is TargetEncoder and BinaryEncoder in sklearn category_encoders?
Target encoding maps the categorical variable to the mean of the target variable. As it uses the target, steps must be taken to avoid overfitting (usually done with smoothing). 
Binary encoding converts each integer into binary digits with each binary digit having its one column. It is essentially a form of feature hashing.
Both help with lowering the cardinality of categorical variables which helps improve some model performance, most notably with tree-based models.
I've been looking for a way to vectorize categorical variable and then I've come across category_encoders. It supports multiple ways to categorize.
I tried TargetEncoder and BinaryEncoder but the docs doesn't explain much about the working of it?
I really appreciate if anyone could explain how target encoder and binary encoder work and how they are different from one hot encoding?