How to generate training instances from multivariate normal distribution?
This can be done by using inverse transformation theorem
import numpy as np
import pandas as pd
from sklearn.datasets import make_classification
from sklearn.preprocessing import QuantileTransformer

def make_gausssians_binary_classification(n_samples= 1000,
                                          n_features= 5,
                                          n_informative= 5,
                                          n_redundant= 0,
                                          random_state= 42
                                          ):

    X, y = make_classification(n_samples= n_samples,
                           n_features= n_features,
                           n_informative= n_informative,
                           n_redundant= n_redundant,
                           random_state= random_state)

    transformation = QuantileTransformer(output_distribution= &quot;normal&quot;).fit(X)

    X_normal = transformation.transform(X)

    return X_normal, y


X, y = make_gausssians_binary_classification(n_samples=10000)