羅
羅左欣 8 years ago
要熟悉的概念有"貝式分類"和"k位最近鄰居法" !
- 20150826 [學習筆記] R與資料探勘 (3)
- 整理自 R軟體與資料探勘之開發與應用 -- 貝式分類、k位最近鄰居法
- [ GitHub ]
- （四）貝式分類(Naïve Bayes)
- [前置作業] 基本概念
- STAGE 1 : 訓練階段
- STAGE 2 : 測試階段
- STAGE 3 : 評估階段
- （五）k位最近鄰居法(k-Nearest Neighbor)
- [前置作業] 基本概念
- STAGE 1 : 訓練階段
- STAGE 2 : 測試階段
- STAGE 3 : 運算階段
- STAGE 4 : 評估階段
整理自 R軟體與資料探勘之開發與應用 -- 貝式分類、k位最近鄰居法
R 軟體安裝(略過)R Studio軟體安裝(略過)START : 訓練資料和測試資料(完成)（一）倒傳遞類神經網路(Back-Propagation Neural Network)(完成)（二）決策樹(Decision Tree)（三）支援向量機(Support Vector Machine)(完成)（四）貝式分類(Naïve Bayes)
install.packages("e1071")library("e1071")# training stagetrainingdata <- read.csv(file.choose())nb.model <- naiveBayes(factor(Species)~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=trainingdata)print(nb.model)# testing stagetestingfile <- read.csv(file.choose())testingdata <- subset(testingfile, select = -Species)testingtarget <- testingfile$Speciesresults <- predict(nb.model, newdata = testingdata)print(results)# accuracytable(results, testingtarget)accuracy <- sum(testingtarget == results)/ length(testingtarget)sprintf("%.2f%%", accuracy * 100)（五）k位最近鄰居法(k-Nearest Neighbor)
install.packages("class")library("class")# training datatrainingfile <- read.csv(file.choose())trainingdata <- subset(trainingfile, select = -Species)trainingtarget <- trainingfile$Species# testing stagetestingfile <- read.csv(file.choose())testingdata <- subset(testingfile, select = -Species)testingtarget <- testingfile$Species# computationresults <- knn(trainingdata, testingdata, trainingtarget, k = 3, prob=TRUE)print(results)# accuracytable(results, testingtarget)accuracy <- sum(testingtarget == results) /length(testingtarget)sprintf("%.2f%%", accuracy * 100)