feature scaling in python

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1from sklearn.preprocessing import MinMaxScaler
2scaler = MinMaxScaler()
3from sklearn.linear_model import Ridge
4X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,
5                                                   random_state = 0)
6
7X_train_scaled = scaler.fit_transform(X_train)
8X_test_scaled = scaler.transform(X_test)
9

1# Scaling features to a range using MinMaxScaler
2
3X_train = np.array([[ 1., -1.,  2.],
4                    [ 2.,  0.,  0.],
5                    [ 0.,  1., -1.]])
6
7min_max_scaler = preprocessing.MinMaxScaler()
8X_train_minmax = min_max_scaler.fit_transform(X_train)
9X_train_minmax
10# array([[0.5		, 0.		, 1.	    ],
11#        [1.		, 0.5		, 0.33333333],
12#        [0.		, 1.		, 0.		]])
13
14X_test = np.array([[-3., -1.,  4.]])
15X_test_minmax = min_max_scaler.transform(X_test)
16X_test_minmax
17# array([[-1.5		,	0.		, 	1.66666667]])
18
19min_max_scaler.scale_
20# array([0.5       , 0.5       , 0.33...])
21
22min_max_scaler.min_
23# array([0.       , 0.5       , 0.33...])

1# Scaling features to a range using MaxAbsScaler
2
3X_train = np.array([[ 1., -1.,  2.],
4                    [ 2.,  0.,  0.],
5                    [ 0.,  1., -1.]])
6
7max_abs_scaler = preprocessing.MaxAbsScaler()
8X_train_maxabs = max_abs_scaler.fit_transform(X_train)
9X_train_maxabs
10# array([[ 0.5, -1.,  1. ],
11#        [ 1. ,  0. ,  0. ],
12#        [ 0. ,  1. , -0.5]])
13X_test = np.array([[ -3., -1.,  4.]])
14X_test_maxabs = max_abs_scaler.transform(X_test)
15X_test_maxabs
16# array([[-1.5, -1. ,  2. ]])
17max_abs_scaler.scale_
18# array([2.,  1.,  2.])

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