问在机器学习中，有没有一种方法可以归因于缺失值？

EN

data = pd.read_csv("D:/Iris_classification/train.csv")

#Shuffle the data and reset the index
from sklearn.utils import shuffle
data = shuffle(data).reset_index(drop = True)  

#Create Independent and dependent matrices
X = data.iloc[:, [0, 1, 2, 3]].values 
y = data.iloc[:, 4].values

#train_test_split
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 50, random_state = 0)

#Standardize the data
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()

X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)

#Impute missing values at random
prop = int(X_train.size * 0.5) #Set the % of values to be replaced
prop1 = int(X_test.size * 0.5)

a = [random.choice(range(X_train.shape[0])) for _ in range(prop)] #Randomly choose indices of the numpy array
b = [random.choice(range(X_train.shape[1])) for _ in range(prop)]

X1_train[a, b] = np.NaN
X1_test[c, d] = np.NaN

X_train_filled = KNN(3).complete(X_train)
X_test_filled = KNN(3).complete(X_test