Standardize vs Normalize
import numpy as np
ex = np.array([0, 1, 2, 3, 4, 5])
# standardize
print('standardized:', (ex - ex.mean()) / ex.std())
# normalize
print('normalized:', (ex - ex.min()) / (ex.max() - ex.min()))
standardized: [-1.46385011 -0.87831007 -0.29277002 0.29277002 0.87831007 1.46385011]
normalized: [0. 0.2 0.4 0.6 0.8 1. ]
X_train = np.random.randn(200, 2)
X_test = np.random.randn(200, 2)
Standardize
from sklearn.preprocessing import MinMaxScaler
mms = MinMaxScaler()
X_train_norm = mms.fit_transform(X_train)
X_test_norm = mms.transform(X_test)
Normalize
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)