分類算法，使用R

Question

文字，我想使用的文字「描述」和「類」

下面我使用的腳本歷史數據類新文檔的預測，但對於新的文件，我想預測我沒有越來越好的準確性，任何人都可以幫助我瞭解哪種算法可以用來提高準確性。請指教。

library(plyr) 
library(tm) 
library(e1071) 

setwd("C:/Data") 

past <- read.csv("Past - Copy.csv",header=T,na.strings=c("")) 
future <- read.csv("Future - Copy.csv",header=T,na.strings=c("")) 

training <- rbind.fill(past,future) 

Res_Desc_Train <- subset(training,select=c("Class","Description")) 

##Step 1 : Create Document Matrix of ticket Descriptions available past data 

docs <- Corpus(VectorSource(Res_Desc_Train$Description)) 
docs <-tm_map(docs,content_transformer(tolower)) 

#remove potentially problematic symbols 
toSpace <- content_transformer(function(x, pattern) { return (gsub(pattern, " ", x))}) 
removeSpecialChars <- function(x) gsub("[^a-zA-Z0-9 ]","",x) 
docs <- tm_map(docs, content_transformer(tolower)) 
docs <- tm_map(docs, removeNumbers) 
docs <- tm_map(docs, removePunctuation) 
docs <- tm_map(docs, stripWhitespace) 
docs <- tm_map(docs, removeWords, stopwords('english')) 


#inspect(docs[440]) 
dataframe<-data.frame(text=unlist(sapply(docs, `[`, "content")), stringsAsFactors=F) 

dtm <- DocumentTermMatrix(docs,control=list(stopwords=FALSE,wordLengths =c(2,Inf))) 

##Let's remove the variables which are 95% or more sparse. 
dtm <- removeSparseTerms(dtm,sparse = 0.95) 

Weighteddtm <- weightTfIdf(dtm,normalize=TRUE) 
mat.df <- as.data.frame(data.matrix(Weighteddtm), stringsAsfactors = FALSE) 
mat.df <- cbind(mat.df, Res_Desc_Train$Class) 
colnames(mat.df)[ncol(mat.df)] <- "Class" 
Assignment.Distribution <- table(mat.df$Class) 

Res_Desc_Train_Assign <- mat.df$Class 

Assignment.Distribution <- table(mat.df$Class) 

### Feature has different ranges, normalizing to bring ranges from 0 to 1 
### Another way to standardize using z-scores 

normalize <- function(x) { 
    y <- min(x) 
    z <- max(x) 
    temp <- x - y 
    temp1 <- (z - y) 
    temp2 <- temp/temp1 
    return(temp2) 
} 
#normalize(c(1,2,3,4,5)) 

num_col <- ncol(mat.df)-1 
mat.df_normalize <- as.data.frame(lapply(mat.df[,1:num_col], normalize)) 
mat.df_normalize <- cbind(mat.df_normalize, Res_Desc_Train_Assign) 
colnames(mat.df_normalize)[ncol(mat.df_normalize)] <- "Class" 

#names(mat.df) 
outcomeName <- "Class" 

train = mat.df_normalize[c(1:nrow(past)),] 
test = mat.df_normalize[((nrow(past)+1):nrow(training)),] 


train$Class <- as.factor(train$Class) 

###SVM Model 
x <- subset(train, select = -Class) 
y <- train$Class 
model <- svm(x, y, probability = TRUE) 
test1 <- subset(test, select = -Class) 
svm.pred <- predict(model, test1, decision.values = TRUE, probability = TRUE) 
svm_prob <- attr(svm.pred, "probabilities") 

finalresult <- cbind(test,svm.pred,svm_prob) 

Answer 1

讓我們嘗試調整您的SVM模型？

您正在使用默認參數運行模型，因此無法獲得更好的準確性。運行模型是一個迭代過程，您可以更改參數，運行模型，檢查準確性，然後再重複整個過程。

model <- tune(svm, train.x=x, train.y=y, kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.5,1,2))) 
print(model) 
#select values of cost & gamma from here and pass it to tuned_model 

tuned_model <- svm(x, y, kernel="radial", cost=<cost_from_tune_model_output>, gamma=<gamma_from_tune_model_output>) 
#now check accuracy of this model using test dataset and accordingly adjust tune parameter. Repeat the whole process again. 

希望這會有所幫助！