12.scikit-learn中的Scaler

import numpy as np
from sklearn import datasets

iris = datasets.load_iris()

X = iris.data
y = iris.target

X[:10,:]

array([[5.1, 3.5, 1.4, 0.2],
       [4.9, 3. , 1.4, 0.2],
       [4.7, 3.2, 1.3, 0.2],
       [4.6, 3.1, 1.5, 0.2],
       [5. , 3.6, 1.4, 0.2],
       [5.4, 3.9, 1.7, 0.4],
       [4.6, 3.4, 1.4, 0.3],
       [5. , 3.4, 1.5, 0.2],
       [4.4, 2.9, 1.4, 0.2],
       [4.9, 3.1, 1.5, 0.1]])

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)

from sklearn.preprocessing import StandardScaler
standardscaler = StandardScaler()
standardscaler.fit(X_train)

StandardScaler(copy=True, with_mean=True, with_std=True)

standardscaler.mean_

array([5.83416667, 3.08666667, 3.70833333, 1.17      ])

standardscaler.scale_

array([0.81019502, 0.44327067, 1.76401924, 0.75317107])

X_train = standardscaler.transform(X_train)
X_test_standard  = standardscaler.transform(X_test)

from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)

knn_clf.score(X_test_standard, y_test)