Sklearn——对数据标准化(Normalization)

1.前言

由于数据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

2.数据标准化

from sklearn import preprocessing #导入用于数据标准化的模块
import numpy as np

data = np.array([[13,54,7,-5],
                 [67,98,11,34],
                [-56,49,22,39]],dtype = np.float64)
print(data)
print(preprocessing.scale(data))     #preprocessing.scale实现数据标准化

#
[[ 13.  54.   7.  -5.]
 [ 67.  98.  11.  34.]
 [-56.  49.  22.  39.]]
[[ 0.09932686 -0.59050255 -0.99861783 -1.40657764]
 [ 1.17205693  1.40812146 -0.36791183  0.57618843]
 [-1.27138379 -0.81761891  1.36652966  0.83038921]]

数据标准化后服从均值为0，方差为1的正太分布

data_ = preprocessing.scale(data)
print(data_.mean(axis = 0))    
print(data_.std(axis = 0))

3.对比标准化前后

from sklearn import preprocessing    #导入用于数据标准化的模块
import numpy as np
from sklearn.model_selection import train_test_split   
from sklearn.datasets.samples_generator import make_classification    #用于生成数据的模块
from sklearn.svm import SVC   

import matplotlib.pyplot as plt


X, y = make_classification(n_samples=400,n_features=2,n_redundant=0,n_informative=2,random_state=42,n_clusters_per_class=1,scale=100)   #特征个数= n_informative（） + n_redundant + n_repeated
plt.scatter(X[:,0],X[:,1],c=y)

3.1.数据标准化前

x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
model = SVC()
model.fit(x_train, y_train)
print("分类准确度:",model.score(x_test, y_test))

#输出
分类准确度: 0.48333333333333334

标准化前的预测准确率只有0.48

3.2.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.

X = preprocessing.scale(X)
x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.3)
model = SVC()
model.fit(x_train, y_train)
print("分类准确度:",model.score(x_test, y_test))

#输出
分类准确度: 0.9166666666666666

标准化后的预测准确率提升至0.92