scikit normalize
from sklearn.preprocessing import normalize X_normalized = normalize(X, norm='l2', *, axis=1, copy=True, return_norm=False)
scikit normalize
from sklearn.preprocessing import normalize X_normalized = normalize(X, norm='l2', *, axis=1, copy=True, return_norm=False)
feature scaling in python
from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() from sklearn.linear_model import Ridge X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, random_state = 0) X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test)
normalizer in sklearn
class sklearn.preprocessing.Normalizer(norm='l2', *, copy=True)
Scaling features to a range
# 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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