How to add noise to supervised (binary-classifier)? Note: The question is not about validating/testing a trained model.

Say i have an unlabeled features set, I want to approximate the true labels (for the sake of argument lets assume it's a binary classification prob).

I also have a trained model to predict the labels.
Now I want to use this model (since it is trained on the true labels, it should approx. my distribution)  along with some added noise (maybe gaussian) to generate the labels for this unknown dataset.
This should always result in a fixed effect size.

How can I do that? My features are mix of categorical and continuous values.   

Would be awesome to illustrate the additive noise via python numpy package.

Apologies, I am a rookie!
I'm not sure why/where you want to apply the noise, but if you want to add some Gaussian noise to a variable, you can do this:

import numpy as np

target_dims = your_target.shape
noise = np.random.rand(target_dims)
noisy_target = your_target + noise


Now use the noisy_target as input to your model.