【原創(chuàng)】R語言svm決策樹模型分析空氣質(zhì)量附代碼數(shù)據(jù)

1、有問題到淘寶找“大數(shù)據(jù)部落”就可以了data= read.csv (air.csv , skip = 1)head (data)# label X1 X2 X3 X4 X5 X6 X7 Y1 Y2 X8 X9X10# 1 北京 14 50 102 81 46965 3.6 203 186 124 23014.60 16410.54 2170.50# 2 天津 29 42 117 70 94867 3.1 142 216 104 16538.19 11946.88 1547.00# 3 石家莊 47 51 147 89 88167 4.3 148 180 123 5440.60 15848.00

2、 1028.84# 4 太原 71 38 114 62 79456 3.1 131 230 94 2735.34 6999.00 367.39# 5 呼和浩特 34 39 103 43 55970 3.2 145 276 87 3090.52 17224.00 238.58# 6 沈陽 66 48 115 72 98871 2.2 155 207 107 7280.49 12948.00 730.41#X11 X12 X13 X14 X15# 1 561.90 342.40 0.26 4542.64 0.20# 2 273.62 229.03 0.18 7704.22 0.47# 3 219.

3、53 138.52 0.21 2452.40 0.45# 4 113.30 161.88 0.31 1020.18 0.37# 5 85.39 49.58 0.36 867.08 0.28# 6 181.70 140.33 0.25 3474.18 0.48分析分為兩個部分第一部分：#數(shù)據(jù)導(dǎo)入與處理#data$Y1 365*0.8 ,空氣好,空氣差,)#構(gòu)建因變量data$Y1- as.factor (data$Y1) #將因變量格式轉(zhuǎn)為因子型#構(gòu)建訓(xùn)練集、測試集#分層抽樣，70%作為訓(xùn)練集，30%作為測試集#library(sampling)trainindex= sample (1: nr

4、ow(data), nrow (data)* 0.7 )train-datatrainindex, # test-data-trainindex, #去除price變量的訓(xùn)練集 去除price變量的測試集有問題到淘寶找“大數(shù)據(jù)部落”就可以了根據(jù)空氣的指標(biāo)（x1x7 ）將30個城市分成兩類，y1用來檢驗分類的效果（可以將y1 的指標(biāo)轉(zhuǎn)化成0,1來表示該城市空氣好或壞）；初步打算使用決策樹方法和SVM方法；編程的步驟盡可能完善，從初始的數(shù)據(jù)預(yù)處理，數(shù)據(jù)可視化，到建模，檢驗，優(yōu)化都不能缺少；如果編程需要訓(xùn)練數(shù)據(jù)集和預(yù)測數(shù)據(jù)集，而個數(shù)太少，我可以提供前幾年的數(shù)據(jù)作為訓(xùn) 練（這個不知你需不需要）。#訓(xùn)I

5、練svm模型#install.packages(e1071) #安裝軟件包library (e1071) # 加載軟件包一、使用線性核函數(shù)去擬合SVM模型1)模型擬合svmfit1- svm(Y1., data= train, kernel= linear ,cost= 10, scale= F)#kernel=linear #選擇線性核函數(shù)scale=F #對數(shù)據(jù)不進行標(biāo)準(zhǔn)化處理，支持向量機的的損失函數(shù)為凸函數(shù)，是否標(biāo)準(zhǔn)化不影響最優(yōu)解，但標(biāo)準(zhǔn)化之后可以使求解速度變快cost=10 #參數(shù)代表犯錯的成本，越大模型對誤差的懲罰越大，生成的分類邊界越復(fù)雜summary ( svmfit1 )# C

6、all:# svm(formula = Y1 .,data = train, kernel = linear, cost = 10,# scale = F)# Parameters:# SVM-Type: C-classification# SVM-Kernel: linear# cost: 10# gamma: 0.02173913# Number of Support Vectors: 8# ( 3 5 )# Number of Classes: 2有問題到淘寶找“大數(shù)據(jù)部落”就可以了# Levels:# 空氣差空氣好2)對svmfitl模型進行改進，選擇最優(yōu)的cost值set.seed

7、( 1) #設(shè)置種子，保證每次運行結(jié)果一致tune.out- tune (svm,Y1., data= train, kernel = linear ,ranges = list ( cost= c( 0.001 , 0.01 , 0.1 , 1, 5, 10, 100) summary (tune.out )# Parameter tuning of svm:# - sampling method: 10-fold cross validation# - best parameters:# cost# 0.1# - best performance: 0.05# - Detailed per

8、formance results:# cost error dispersion# 1 1e-03 0.30 0.2581989# 2 1e-02 0.30 0.2581989# 3 1e-01 0.05 0.1581139# 4 1e+00 0.05 0.1581139# 5 5e+00 0.05 0.1581139# 6 1e+01 0.05 0.1581139# 7 1e+02 0.05 0.1581139best.mode1-tune.out$best.model #得到最優(yōu)模型summary ( best.mode1 )# Call:# best.tune(method = svm,

9、 train.x = Y1 .,data = train, ranges = lis t(cost = c(0.001,#0.01,0.1, 1,5, 10, 100), kernel = linear)# Parameters:# SVM-Type: C-classification# SVM-Kernel: linear# cost: 0.1# gamma: 0.02173913# Number of Support Vectors: 11有問題到淘寶找“大數(shù)據(jù)部落”就可以了# ( 5 6 )# Number of Classes: 2# Levels:# 空氣差空氣好3)模型評估ypre

10、d- predict(best.mode1,test)table (predict=ypred, ture= test$Y1)#ture# predict#空氣差#空氣好空氣差空氣好6102# 訓(xùn)I練 decision tree模型# library (rpart)grow tree fit - rpart (Y1 .-label-Y1, method= class , data= data) printcp (fit) # display the results# Classification tree:# rpart(formula = Y1 . - label - Y1, data =

11、data, method = class)# Variables actually used in tree construction:# 1 X4# Root node error: 9/30 = 0.3# n= 30# 1 0.88889# 2 0.01000plotcp (fit)# CP nsplit rel error xerror xstd0 1.00000 1.00000 0.278891 0.11111 0.22222 0.15181# visualize cross-validation results有問題到淘寶找“大數(shù)據(jù)部落”就可以了size of treeInf0 09

12、4summary (fit) # detailed summary of splits# Call:# rpart(formula = Y1 .-label - Y1, data = data, method = class)# n= 30# CP nsplit rel error xerror xstd# 1 0.88888890 1.0000000 1.0000000 0.2788867# 2 0.01000001 0.1111111 0.2222222 0.1518067# Variable importance# X4 X6 X3 Y2 X2 X8# 23 18 16 16 14 14

13、# Node number 1: 30 observations, complexity param=0.8888889# predicted class= 空氣差 expected loss=0.3 P(node) =1# class counts: 219# probabilities: 0.700 0.300# left son=2 (20 obs) right son=3 (10 obs)# Primary splits:# X4 50 to the right, improve=10.800000, (0 missing)#X6 1.45to the right,improve=10.690910, (0 missing)#Y2 74.5to the ri