INNER CODE UNIT · Python
X_miss_mcar
SalvatoreRa/tutorial · machine learning/scripts/MNAR.py:119
X_miss_mcar = produce_NA(df.iloc[:, :-1], p_miss=perc,
mecha="MNAR", opt="logistic", p_obs=0.5)
X_miss_mcar = X_miss_mcar['X_incomp'].detach().numpy()
X_miss_mcar = np.where(X_miss_mcar=='nan', np.nan, X_miss_mcar )
kf = KFold(n_splits=fold_splits)
for train_index, test_index in kf.split(X_miss_mcar):
X_train, X_test = X_miss_mcar[train_index], X_miss_mcar[test_index]
y_train, y_test = y[train_index], y[test_index]
# we are starting now the process of imputation
# for each case we will measure the score
# the score represents the accuracy in this case