scikit-learn机器学习(三)多项式回归(二阶，三阶，九阶)

我们仍然使用披萨直径的价格的数据

import matplotlib

matplotlib.rcParams['font.sans-serif']=[u'simHei']

matplotlib.rcParams['axes.unicode_minus']=False

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.preprocessing import PolynomialFeatures

X_train = [[6],[8],[10],[14],[18]]

y_train = [[7],[9],[13],[17.5],[18]]

X_test = [[6],[8],[11],[16]]

y_test = [[8],[12],[15],[18]]

LR = LinearRegression()

LR.fit(X_train,y_train)

xx = np.linspace(0,26,100)

yy = LR.predict(xx.reshape(xx.shape[0],1))

plt.plot(xx,yy)

二阶多项式回归

# In[1] 二次回归，二阶多项式回归

#PolynomialFeatures转换器可以用于为一个特征表示增加多项式特征

quadratic_featurizer = PolynomialFeatures(degree=2)

X_train_quadratic = quadratic_featurizer.fit_transform(X_train)

X_test_quadratic = quadratic_featurizer.transform(X_test)

regressor_quadratic = LinearRegression()

regressor_quadratic.fit(X_train_quadratic,y_train)

xx_quadratic = quadratic_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_quadratic = regressor_quadratic.predict(xx_quadratic)

plt.plot(xx,yy_quadratic,c='r',linestyle='--')

# In[2] 图参数，输出结果

plt.title("披萨价格和直径的关系")

plt.xlabel("直径")

plt.ylabel("价格")

plt.axis([0,25,0,25])

plt.grid(True)

plt.scatter(X_train,y_train)

print("X_train\n",X_train)

print("X_train_quadratic\n",X_train_quadratic)

print("X_test\n",X_test)

print("X_test_quadratic\n",X_test_quadratic)

print("简单线性规划R方",LR.score(X_test,y_test))

print("二阶多项式回归R方",regressor_quadratic.score(X_test_quadratic,y_test))

X_train

 [[6], [8], [10], [14], [18]]

X_train_quadratic

 [[  1.   6.  36.]

 [  1.   8.  64.]

 [  1.  10. 100.]

 [  1.  14. 196.]

 [  1.  18. 324.]]

X_test

 [[6], [8], [11], [16]]

X_test_quadratic

 [[  1.   6.  36.]

 [  1.   8.  64.]

 [  1.  11. 121.]

 [  1.  16. 256.]]

简单线性规划R方 0.809726797707665

三阶多项式回归

# In[3] 尝试三阶多项式回归

cubic_featurizer = PolynomialFeatures(degree=3)

X_train_cubic = cubic_featurizer.fit_transform(X_train)

X_test_cubic = cubic_featurizer.transform(X_test)

regressor_cubic = LinearRegression()

regressor_cubic.fit(X_train_cubic,y_train)

xx_cubic = cubic_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_cubic = regressor_cubic.predict(xx_cubic)

plt.plot(xx,yy_cubic,c='g',linestyle='--')

plt.show()

print("X_train\n",X_train)

print("X_train_cubic\n",X_train_cubic)

print("X_test\n",X_test)

print("X_test_cubic\n",X_test_cubic)

print("三阶多项式回归R方",regressor_cubic.score(X_test_cubic,y_test))

X_train

 [[6], [8], [10], [14], [18]]

X_train_cubic

 [[1.000e+00 6.000e+00 3.600e+01 2.160e+02]

 [1.000e+00 8.000e+00 6.400e+01 5.120e+02]

 [1.000e+00 1.000e+01 1.000e+02 1.000e+03]

 [1.000e+00 1.400e+01 1.960e+02 2.744e+03]

 [1.000e+00 1.800e+01 3.240e+02 5.832e+03]]

X_test

 [[6], [8], [11], [16]]

X_test_cubic

 [[1.000e+00 6.000e+00 3.600e+01 2.160e+02]

 [1.000e+00 8.000e+00 6.400e+01 5.120e+02]

 [1.000e+00 1.100e+01 1.210e+02 1.331e+03]

 [1.000e+00 1.600e+01 2.560e+02 4.096e+03]]

三阶多项式回归R方 0.8356924156036954

九阶多项式回归

# In[4] 尝试九阶多项式回归

nine_featurizer = PolynomialFeatures(degree=9)

X_train_nine = nine_featurizer.fit_transform(X_train)

X_test_nine = nine_featurizer.transform(X_test)

regressor_nine = LinearRegression()

regressor_nine.fit(X_train_nine,y_train)

xx_nine = nine_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_nine = regressor_nine.predict(xx_nine)

plt.plot(xx,yy_nine,c='k',linestyle='--')

plt.show()

print("X_train\n",X_train)

print("X_train_nine\n",X_train_nine)

print("X_test\n",X_test)

print("X_test_nine\n",X_test_nine)

print("九阶多项式回归R方",regressor_nine.score(X_test_nine,y_test))

X_train

 [[6], [8], [10], [14], [18]]

X_train_nine

 [[1.00000000e+00 6.00000000e+00 3.60000000e+01 2.16000000e+02

  1.29600000e+03 7.77600000e+03 4.66560000e+04 2.79936000e+05

  1.67961600e+06 1.00776960e+07]

 [1.00000000e+00 8.00000000e+00 6.40000000e+01 5.12000000e+02

  4.09600000e+03 3.27680000e+04 2.62144000e+05 2.09715200e+06

  1.67772160e+07 1.34217728e+08]

 [1.00000000e+00 1.00000000e+01 1.00000000e+02 1.00000000e+03

  1.00000000e+04 1.00000000e+05 1.00000000e+06 1.00000000e+07

  1.00000000e+08 1.00000000e+09]

 [1.00000000e+00 1.40000000e+01 1.96000000e+02 2.74400000e+03

  3.84160000e+04 5.37824000e+05 7.52953600e+06 1.05413504e+08

  1.47578906e+09 2.06610468e+10]

 [1.00000000e+00 1.80000000e+01 3.24000000e+02 5.83200000e+03

  1.04976000e+05 1.88956800e+06 3.40122240e+07 6.12220032e+08

  1.10199606e+10 1.98359290e+11]]

X_test

 [[6], [8], [11], [16]]

X_test_nine

 [[1.00000000e+00 6.00000000e+00 3.60000000e+01 2.16000000e+02

  1.29600000e+03 7.77600000e+03 4.66560000e+04 2.79936000e+05

  1.67961600e+06 1.00776960e+07]

 [1.00000000e+00 8.00000000e+00 6.40000000e+01 5.12000000e+02

  4.09600000e+03 3.27680000e+04 2.62144000e+05 2.09715200e+06

  1.67772160e+07 1.34217728e+08]

 [1.00000000e+00 1.10000000e+01 1.21000000e+02 1.33100000e+03

  1.46410000e+04 1.61051000e+05 1.77156100e+06 1.94871710e+07

  2.14358881e+08 2.35794769e+09]

 [1.00000000e+00 1.60000000e+01 2.56000000e+02 4.09600000e+03

  6.55360000e+04 1.04857600e+06 1.67772160e+07 2.68435456e+08

  4.29496730e+09 6.87194767e+10]]

九阶多项式回归R方 -0.09435666704291412

所有代码

# -*- coding: utf-8 -*-

import matplotlib

matplotlib.rcParams['font.sans-serif']=[u'simHei']

matplotlib.rcParams['axes.unicode_minus']=False

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.preprocessing import PolynomialFeatures

X_train = [[6],[8],[10],[14],[18]]

y_train = [[7],[9],[13],[17.5],[18]]

X_test = [[6],[8],[11],[16]]

y_test = [[8],[12],[15],[18]]

LR = LinearRegression()

LR.fit(X_train,y_train)

xx = np.linspace(0,26,100)

yy = LR.predict(xx.reshape(xx.shape[0],1))

plt.plot(xx,yy)

# In[1] 二次回归，二阶多项式回归

#PolynomialFeatures转换器可以用于为一个特征表示增加多项式特征

quadratic_featurizer = PolynomialFeatures(degree=2)

X_train_quadratic = quadratic_featurizer.fit_transform(X_train)

X_test_quadratic = quadratic_featurizer.transform(X_test)

regressor_quadratic = LinearRegression()

regressor_quadratic.fit(X_train_quadratic,y_train)

xx_quadratic = quadratic_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_quadratic = regressor_quadratic.predict(xx_quadratic)

plt.plot(xx,yy_quadratic,c='r',linestyle='--')

# In[2] 图参数，输出结果

plt.title("披萨价格和直径的关系")

plt.xlabel("直径")

plt.ylabel("价格")

plt.axis([0,25,0,25])

plt.grid(True)

plt.scatter(X_train,y_train)

print("X_train\n",X_train)

print("X_train_quadratic\n",X_train_quadratic)

print("X_test\n",X_test)

print("X_test_quadratic\n",X_test_quadratic)

print("简单线性规划R方",LR.score(X_test,y_test))

print("二阶多项式回归R方",regressor_quadratic.score(X_test_quadratic,y_test))

# In[3] 尝试三阶多项式回归

cubic_featurizer = PolynomialFeatures(degree=3)

X_train_cubic = cubic_featurizer.fit_transform(X_train)

X_test_cubic = cubic_featurizer.transform(X_test)

regressor_cubic = LinearRegression()

regressor_cubic.fit(X_train_cubic,y_train)

xx_cubic = cubic_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_cubic = regressor_cubic.predict(xx_cubic)

plt.plot(xx,yy_cubic,c='g',linestyle='--')

plt.show()

print("X_train\n",X_train)

print("X_train_cubic\n",X_train_cubic)

print("X_test\n",X_test)

print("X_test_cubic\n",X_test_cubic)

print("三阶多项式回归R方",regressor_cubic.score(X_test_cubic,y_test))

# In[4] 尝试九阶多项式回归

nine_featurizer = PolynomialFeatures(degree=9)

X_train_nine = nine_featurizer.fit_transform(X_train)

X_test_nine = nine_featurizer.transform(X_test)

regressor_nine = LinearRegression()

regressor_nine.fit(X_train_nine,y_train)

xx_nine = nine_featurizer.transform(xx.reshape(xx.shape[0],1))

yy_nine = regressor_nine.predict(xx_nine)

plt.plot(xx,yy_nine,c='k',linestyle='--')

plt.show()

print("X_train\n",X_train)

print("X_train_nine\n",X_train_nine)

print("X_test\n",X_test)

print("X_test_nine\n",X_test_nine)

print("九阶多项式回归R方",regressor_nine.score(X_test_nine,y_test))

scikit-learn机器学习(三)多项式回归(二阶，三阶，九阶)的更多相关文章

(原创)（三）机器学习笔记之Scikit Learn的线性回归模型初探
一.Scikit Learn中使用estimator三部曲 1. 构造estimator 2. 训练模型:fit 3. 利用模型进行预测:predict 二.模型评价模型训练好后,度量模型拟合效果的 ...
Scikit Learn: 在python中机器学习
转自:http://my.oschina.net/u/175377/blog/84420#OSC_h2_23 Scikit Learn: 在python中机器学习 Warning 警告:有些没能理解的 ...
(原创)（四）机器学习笔记之Scikit Learn的Logistic回归初探
目录 5.3 使用LogisticRegressionCV进行正则化的 Logistic Regression 参数调优一.Scikit Learn中有关logistics回归函数的介绍 1. 交叉 ...
scikit learn 模块调参 pipeline+girdsearch 数据举例：文档分类（python代码）
scikit learn 模块调参 pipeline+girdsearch 数据举例:文档分类数据集 fetch_20newsgroups #-*- coding: UTF-8 -*- import ...
TensorFlow 便捷的实现机器学习三
TensorFlow 便捷的实现机器学习三 MNIST 卷积神经网络 Fly Overview Enabling Logging with TensorFlow Configuring a Vali ...
【机器学习】多项式回归sklearn实现
[机器学习]多项式回归原理介绍 [机器学习]多项式回归python实现 [机器学习]多项式回归sklearn实现使用sklearn框架实现多项式回归.使用框架更方便,可以少写很多代码. 使用一个简单 ...
【机器学习】多项式回归python实现
[机器学习]多项式回归原理介绍 [机器学习]多项式回归python实现 [机器学习]多项式回归sklearn实现使用python实现多项式回归,没有使用sklearn等机器学习框架,目的是帮助理解算 ...
Scikit Learn
Scikit Learn Scikit-Learn简称sklearn,基于 Python 语言的,简单高效的数据挖掘和数据分析工具,建立在 NumPy,SciPy 和 matplotlib 上.
机器学习框架Scikit Learn的学习
一安装安装pip 代码如下:# wget "https://pypi.python.org/packages/source/p/pip/pip-1.5.4.tar.gz#md5=83 ...

随机推荐

Tcp/IP协议详讲
TCP/IP协议分层详解目录 TCP/IP 和 ISO/OSI TCP/IP分层模型数据的封装与分用其他相关概念 TCP/IP 通信传输流负责传输的 IP 协议正文回到顶部 TCP/IP ...
Windows10官方正版系统的安装、激活、升级、U盘制作，无毒无害无捆绑无风险教程
一般电脑系统出了其他问题或电脑用久太卡了,可以选择此类方法解决系统卡顿问题,重置电脑系统或也可以恢复出厂设置如果出现重置找不到恢复环境问题可以通过下载系统镜像来解决,进入 MSDN 网站下载所需系 ...
layer弹出框中icon数字参数说明
前言icon参数为0,如下代码: layer.msg(}); 运行结果如图: icon参数为1,如下图 icon参数为2,如下图: icon参数为3,如下图: icon参数为4,如下图: icon参数 ...
查看postgresql的日志show queries log in PostgreSQL?
原文:https://tableplus.io/blog/2018/10/how-to-show-queries-log-in-postgresql.html -------------------- ...
Spring-HelloSpring-IOC创建对象的方式
3.HelloSpring 思考 Hello 对象是谁创建的 ? [ hello 对象是由Spring创建的 ] Hello 对象的属性是怎么设置的 ? [hello 对象的属性是由Spring容器设 ...
图像Image类、打印Printing相关
一.Images 1.概述 Image 类为Bitmap(位图) 和 Metafile(矢量图) 的类提供功能的抽象基类.Image类不能直接创建对象的,但Image.FromFile()返回的是Bi ...
HDU 6143 - Killer Names | 2017 Multi-University Training Contest 8
/* HDU 6143 - Killer Names [ DP ] | 2017 Multi-University Training Contest 8 题意: m个字母组成两个长为n的序列,两序列中 ...
Codeforces Round #590 (Div. 3)【D题：26棵树状数组维护字符出现次数】
A题题意:给你 n 个数 , 你需要改变这些数使得这 n 个数的值相等 , 并且要求改变后所有数的和需大于等于原来的所有数字的和 , 然后输出满足题意且改变后最小的数值. AC代码: #includ ...
jQuery.extend([deep], target, object1, [objectN])
jQuery.extend([deep], target, object1, [objectN]) 概述用一个或多个其他对象来扩展一个对象,返回被扩展的对象.直线电机如果不指定target,则给j ...
002_linux驱动之_register_chrdev注册字符设备
(一)解析:register_chrdev函数和unregister_chrdev函数 (二)register_chrdev函数原型 int register_chrdev(unsigned int ...

scikit-learn机器学习(三)多项式回归(二阶，三阶，九阶)

scikit-learn机器学习(三)多项式回归(二阶，三阶，九阶)的更多相关文章

随机推荐

热门专题