題目：有效分類不同的酒種 - SVM 演算法

資料輸入 - Wine Dataset

從 LIBSVM Data 取得 wine dataset (https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/wine.scale)。 透過read.libsvm.R 讀入資料並整理之。

#read in the data and convert to dataframe
source( 'read.libsvm.R' )
wine = read.libsvm( 'wine.scale', 13 )
wine = as.data.frame(wine)

#reassign attributes
names(wine) = c("type","alc","acid","ash","alk","mag","phenols","flav","nonflav","proanth","color","hue","OD","proline")

#encode wine type as factor
wine$type = as.factor(wine$type)

套件執行

library(e1071)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(lattice)
library(ggplot2)
library(caret)

繪圖

透過繪圖找出能最能明顯區分出不同酒種的兩個變量。

Scatter Plot - Wine Dataset

先繪製所有變量的 scatter plot，找出較能明顯區分出不同酒種的變量組合。

#plot wine dataset
plot(wine)

Scatter Plot - Specific Variable Sets

取出上述繪圖中，較能明顯區分出不同酒種的變量組合再做 plot，找出最能明顯區分出不同酒種的變量組合。

#plot(wine$acid, wine$OD, col=wine$type)
#plot(wine$acid, wine$phenols, col=wine$type)
#plot(wine$OD, wine$phenols, col=wine$type)
#plot(wine$color, wine$hue, col=wine$type)
#plot(wine$alc, wine$color, col=wine$type)
#plot(wine$alc, wine$hue, col=wine$type) 
#plot(wine$alc, wine$flav, col=wine$type) 
#plot(wine$flav, wine$color, col=wine$type)

plot(wine$flav, wine$hue, col=wine$type)

在以上各變量組合的 trial & error 後，發現組合 (flav, hue) 最能明顯區分出不同酒種。

SVM 演算法

首先先建立新的dataset，其中包含因變量類黃酮含量 (flav)、色澤(hue) ，以及自變量酒種 (type)。

#create new, smaller dataset
col = c("flav","hue","type")
dta = wine %>%
  select(col)

其次，建立 testing data。

#create testing samples
testId = sample(nrow(dta),78, replace=FALSE)

x <- subset(dta[testId,], select = -type)
y <- dta$type[testId]

最後，透過 SVM 演算法得出 training model，代入 testing data 以測試成效。透過繪製 confusionMatrix(pred,y) ，得到預測模型準確率高達96%。

#training model
trainingId = dta[-testId,]
svm_model1 = 
  svmfit = svm(type ~ ., data = dta[-testId,])

pred = predict(svm_model1,x)
confusionMatrix(pred,y)
## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  1  2  3
##          1 23  3  0
##          2  4 27  0
##          3  0  0 21
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9103          
##                  95% CI : (0.8238, 0.9632)
##     No Information Rate : 0.3846          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8639          
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: 1 Class: 2 Class: 3
## Sensitivity            0.8519   0.9000   1.0000
## Specificity            0.9412   0.9167   1.0000
## Pos Pred Value         0.8846   0.8710   1.0000
## Neg Pred Value         0.9231   0.9362   1.0000
## Prevalence             0.3462   0.3846   0.2692
## Detection Rate         0.2949   0.3462   0.2692
## Detection Prevalence   0.3333   0.3974   0.2692
## Balanced Accuracy      0.8965   0.9083   1.0000