Sklearn preprocessing.scale

from sklearn import preprocessing
import numpy as np

from sklearn.model_selection import train_test_split

from sklearn.datasets.samples_generator import make_classification

from sklearn.svm import SVC

import matplotlib.pyplot as plt
# a = np.array([[10, 2.7, 3.6],
#              [-100, 5, -2],
#              [120, 20, 40]], dtype=np.float64)
#
# print(preprocessing.scale(a))


X, y = make_classification(
    n_samples=300, n_features=2,
    n_redundant=0, n_informative=2,
    random_state=22, n_clusters_per_class=1, scale=100
)

# plt.scatter(X[:, 0], X[:, 1], c=y)
# plt.show()

X = preprocessing.scale(X)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
clf = SVC()
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

因为生成的数据用的scale为100，所以用来训练测试，准确性不高，用preprocessing.scale后，准确率很高。
https://morvanzhou.github.io/tutorials/machine-learning/sklearn/3-1-normalization/

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