How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How to implement reverse ordinal encoding in a sklearn pipeline and avoid a NotFittedError?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
How can I reverse ordinal encoding within a sklearn pipeline?
I am trying to clean a dataset that contains a lot of missing values from different features that are both numerical and categorical. My idea is the following:
Leave one out encoding on test set with transform
Context: When preprocessing a data set using sklearn, you use fit_transform on the training set and transform on the test set, to avoid data leakage. Using leave one out (LOO) encoding, you need the target variable value to calculate the encoded value of a feature value. When using the LOO encoder in a pipeline, you can apply it to the training set using the fit_transform function, which accepts the features (X) and the target values (y).