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)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.])The function scale provides a quick and easy way to perform
# Standardization
from sklearn import preprocessing
import numpy as np
X_train = np.array([[1., -1., 2.],
[2., 0., 0.],
[0., 1., -1.]])
X_scaled = preprocessing.scale(X_train)
X_scaled
# array([[ 0. ..., -1.22..., 1.33...],
# [ 1.22..., 0. ..., -0.26...],
# [-1.22..., 1.22..., -1.06...]])
# Scaled data has zero mean and unit variance:
X_scaled.mea(axis=0)
# array([0., 0., 0.])
X_scaled.std(axis=0)
# array([1., 1., 1.])
scaler = preprocessing.StandardScaler().fit(X_train)
scaler
# StandardScaler()
scaler.mean_
# array([1. ..., 0. ..., 0.33...])
scaler.scale_
# array([0.81..., 0.81..., 1.24...])
scaler.transform(X_train)
array([[ 0. ..., -1.22..., 1.33...],
[ 1.22..., 0. ..., -0.26...],
[-1.22..., 1.22..., -1.06...]])
X_test = [[-1., 1., 0.]]
scaler.transform(X_test)
# array([[-2.44..., 1.22..., -0.26...]])Copyright © 2021 Codeinu
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