正规化 Normalization

由于资料的偏差与跨度会影响机器学习的成效，因此正规化(标准化)数据可以提升机器学习的成效。首先由例子来讲解:

数据标准化

from sklearn import preprocessing #标准化数据模块
import numpy as np

#建立Array
a = np.array([[10, 2.7, 3.6],
              [-100, 5, -2],
              [120, 20, 40]], dtype=np.float64)

#将normalized后的a打印出
print(preprocessing.scale(a))
# [[ 0.         -0.85170713 -0.55138018]
#  [-1.22474487 -0.55187146 -0.852133  ]
#  [ 1.22474487  1.40357859  1.40351318]]

数据标准化对机器学习成效的影响

加载模块

# 标准化数据模块
from sklearn import preprocessing 
import numpy as np

# 将资料分割成train与test的模块
from sklearn.model_selection import train_test_split

# 生成适合做classification资料的模块
from sklearn.datasets.samples_generator import make_classification 

# Support Vector Machine中的Support Vector Classifier
from sklearn.svm import SVC 

# 可视化数据的模块
import matplotlib.pyplot as plt 

生成适合做Classification数据

#生成具有2种属性的300笔数据
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)

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

数据标准化前

标准化前的预测准确率只有0.477777777778

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))
# 0.477777777778

数据标准化后

数据的单位发生了变化, X 数据也被压缩到差不多大小范围.标准化后的预测准确率提升至0.9

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))
# 0.9