sklearn log transform

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

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# 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.])

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