R中的集中算法集成

dataset = read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.data", sep = ",",header = F, na.strings = "?")head(dataset)sapply(dataset, function(x) sum(is.na(x)))sapply(dataset, class)#分割数据的训练集和测试集set.seed(123)dataset=na.omit(dataset)n=dim(dataset)[1]index=sample(n,round(0.7*n))train=dataset[index,]test=dataset[-index,]dim(train)####有时候，需要转化变量为哑变量，因为在一些挖掘场合，数据不能直接使用因子型的数据knnglmnetsvmxgboost###有些挖掘方法可以使用因子变量，比如：logistic regressionrepartGBMrandomForestdataset2=datasetlibrary(plyr)into_factor = function(x){  if(class(x) == "factor"){    n = length(x)    data.fac = data.frame(x = x,y = 1:n)    output = model.matrix(y~x,data.fac)[,-1]    ## Convert factor into dummy variable matrix  }else{    output = x    ## if x is numeric, output is x  }  output}into_factor(dataset$V4)[1:5,]dataset2=colwise(into_factor)(dataset2)dataset2=do.call(cbind,dataset2)dataset2=as.data.frame(dataset2)dim(dataset2)#####使用logistic回归来进行测试建模和预测，使用glmlogit.model=glm(V16~.,data=train,family = "binomial")logit.response=predict(logit.model,test,type="response")logit.predict=ifelse(logit.response>0.5,"+","-")table(logit.predict,test$V16)accutancy1=mean(logit.predict==test$V16)####使用GBM方法来预测，这里用的是caret,repeat-cv来选择最优树library(caret)ctrl=trainControl(method = "repeatedcv",number = 5,repeats=5)set.seed(300)m_gbm=train(V16~.,data=train,methed="gbm",metric="Kappa",trControl=ctrl)gbm.predict=predict(m_gbm,test)table(gbm.predict,test$V16)accutancy2=mean(gbm.predict==test$V16)####首先测试一个knn模型，不做CV，不做标准化，不做数据类型转化得到的结果，这里，不转换数据类型会把因子类型的变量舍弃，仅保留数据变量library(caret)knn.model1=knn3(V16~.,data=train,k=5)knn.response1=predict(knn.model1,test,class="response")knn.predict1=ifelse(knn.response1[,1]<0.5,"+","-")table(knn.predict1,test$V16)mean(knn.predict1==test$V16)#####经过标准化和数据转换之后的准确率knn.dataset=cbind(colwise(scale)(dataset2[,-38]),V16=as.factor(dataset2$V16))set.seed(123)index=sample(n,round(0.7*n))train.knn=knn.dataset[index,]test.knn=knn.dataset[-index,]knn.model1 = knn3(V16 ~ .,data = train.knn, k = 5)knn.predict1 = predict(knn.model1,test.knn,,type = "class") table(knn.predict1,test.knn$V16)mean(knn.predict1 == test.knn$V16)#####knn CV for k  不管是我的这个程序函数caret,总算出来应该是k=2的时候误差最小,但是实际情况不是这样library(class)cv.knn = function(data,n=5,k){  index = sample(1:5,nrow(data),replace = T)  acc=0  for ( i in 1:5){    ind = index == i    train = data[-ind,]    test = data[ind,]    knn.model1 = knn3(train$V16 ~ .,data = train, k = k)      knn.predict= predict(knn.model1,test,type = "class")     acc[i] = mean(knn.predict == test$V16)  }  mean(acc)}cv.knn(train.knn,3,5)k = 2:20set.seed(123)acc = sapply(k,function(x) cv.knn(train.knn,3,x))plot(k,acc,type = "b")k.final = which.max(acc)knn.model.f = knn3(V16 ~ .,data = train.knn, k = k.final) knn.predict.f = predict(knn.model.f,test.knn,type = "class") table(knn.predict.f,test.knn$V16)mean(knn.predict.f == test.knn$V16)library(caret)fitControl <- trainControl(method = "cv", number = 10)knnTune <- train(x = dataset2[1:37], y = dataset2[,38], method = "knn", preProc = c("center", "scale"),tuneGrid = data.frame(.k = 1:20), trControl = fitControl)############xgboostrequire(xgboost)require(methods)require(plyr)set.seed(123)set.seed(123)index = sample(n,round(0.7*n))train.xg = dataset2[index,]test.xg = dataset2[-index,]label <- as.matrix(train.xg[,38,drop =F])data <- as.matrix(train.xg[,-38,drop =F])data2 <-  as.matrix(test.xg[,-38,drop =F])label2 =  as.matrix(test.xg[,38,drop =F])# weight <- as.numeric(dtrain[[32]]) * testsize / length(label)xgmat <- xgb.DMatrix(data, label = label, missing = -10000)param <- list("objective" = "binary:logistic","bst:eta" = 1,"bst:max_depth" = 2,"eval_metric" = "logloss","silent" = 1,"nthread" = 16 ,"min_child_weight" =1.45)nround =275bst = xgb.train(param, xgmat, nround )res1 = predict(bst,data2)pre1 = ifelse(res1>0.5,1,0)table(pre1,label2)accurancy4 = mean(pre1 ==label2)