隨機森林，使用R

-1

我正在構建用於使用R.執行gcrma標準化後乳腺癌數據的預測模型，我產生的電位預測變量。現在，當我運行RF算法我遇到了以下錯誤隨機森林，使用R

rf_output=randomForest(x=pred.data, y=target, importance = TRUE, ntree = 25001, proximity=TRUE, sampsize=sampsizes) 

Error: Error in randomForest.default(x = pred.data, y = target, importance = TRUE, : Can not handle categorical predictors with more than 53 categories.

代碼：

library(randomForest) 
library(ROCR) 
library(Hmisc) 
library(genefilter) 

setwd("E:/kavya's project_work/final") 
datafile<-"trainset_gcrma.txt" 
clindatafile<-read.csv("mod clinical_details.csv") 

outfile="trainset_RFoutput.txt" 
varimp_pdffile="trainset_varImps.pdf" 
MDS_pdffile="trainset_MDS.pdf" 
ROC_pdffile="trainset_ROC.pdf" 
case_pred_outfile="trainset_CasePredictions.txt" 
vote_dist_pdffile="trainset_vote_dist.pdf" 

data_import=read.table(datafile, header = TRUE, na.strings = "NA", sep="\t") 
clin_data_import=clindatafile 
clincaldata_order=order(clin_data_import[,"GEO.asscession.number"]) 
clindata=clin_data_import[clincaldata_order,] 
data_order=order(colnames(data_import)[4:length(colnames(data_import))])+3 #Order data without first three columns, then add 3 to get correct index in original file 
rawdata=data_import[,c(1:3,data_order)] #grab first three columns, and then remaining columns in order determined above 
header=colnames(rawdata) 

X=rawdata[,4:length(header)] 
ffun=filterfun(pOverA(p = 0.2, A = 100), cv(a = 0.7, b = 10)) 
filt=genefilter(2^X,ffun) 
filt_Data=rawdata[filt,] 



#Get potential predictor variables 
predictor_data=t(filt_Data[,4:length(header)]) 
predictor_names=c(as.vector(filt_Data[,3])) #gene symbol 
colnames(predictor_data)=predictor_names 


target= clindata[,"relapse"] 
target[target==0]="NoRelapse" 
target[target==1]="Relapse" 
target=as.factor(target) 

tmp = as.vector(table(target)) 
num_classes = length(tmp) 
min_size = tmp[order(tmp,decreasing=FALSE)[1]] 
sampsizes = rep(min_size,num_classes) 
rf_output=randomForest(x=pred.data, y=target, importance = TRUE, ntree = 25001, proximity=TRUE, sampsize=sampsizes) 


error:"Error in randomForest.default(x = pred.data, y = target, importance = TRUE, : Can not handle categorical predictors with more than 53 categories."

，因爲我是新來的機器學習我無法繼續。請做好必要的事情。提前。

來源

2016-03-16 Kavya Krishnamurthy

的[同時使用，使用R隨機森林來錯誤]可能的複製（http://stackoverflow.com/questions/33098731/error-coming-while-using-random-forest-using-r） – phiver

很難在不知道數據的話。對所有預測變量運行class或summary，以確保它們不會被意外解釋爲字符或因素。如果你真的有超過53個關卡，你將不得不把它們轉換成二進制變量。例如：

mtcars$automatic <- mtcars$am == 0 
mtcars$manual <- mtcars$am == 1

來源

2016-03-16 09:20:16 sebastianmm

隨機森林，使用R

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