how to normalize data in python sklearn

Scaling features to a range

# Scaling features to a range using MinMaxScaler

X_train = np.array([[ 1., -1.,  2.],
                    [ 2.,  0.,  0.],
                    [ 0.,  1., -1.]])

min_max_scaler = preprocessing.MinMaxScaler()
X_train_minmax = min_max_scaler.fit_transform(X_train)
X_train_minmax
# array([[0.5		, 0.		, 1.	    ],
#        [1.		, 0.5		, 0.33333333],
#        [0.		, 1.		, 0.		]])

X_test = np.array([[-3., -1.,  4.]])
X_test_minmax = min_max_scaler.transform(X_test)
X_test_minmax
# array([[-1.5		,	0.		, 	1.66666667]])

min_max_scaler.scale_
# array([0.5       , 0.5       , 0.33...])

min_max_scaler.min_
# array([0.       , 0.5       , 0.33...])

Posted by: Guest on April-16-2020

Source

# Scaling features to a range using MaxAbsScaler X_train = np.array([[ 1., -1., 2.], [ 2., 0., 0.], [ 0., 1., -1.]]) max_abs_scaler = preprocessing.MaxAbsScaler() X_train_maxabs = max_abs_scaler.fit_transform(X_train) X_train_maxabs # array([[ 0.5, -1., 1. ], # [ 1. , 0. , 0. ], # [ 0. , 1. , -0.5]]) X_test = np.array([[ -3., -1., 4.]]) X_test_maxabs = max_abs_scaler.transform(X_test) X_test_maxabs # array([[-1.5, -1. , 2. ]]) max_abs_scaler.scale_ # array([2., 1., 2.])

Code answers related to "how to normalize data in python sklearn"

Code answers related to "Python"

Browse Popular Code Answers by Language

Answers for "how to normalize data in python sklearn"

Code answers related to "how to normalize data in python sklearn"

Code answers related to "Python"

Python Answers by Framework

Browse Popular Code Answers by Language

Popular Programming Languages

Advertisements

Company

Compilers

Help

Connect with us