决策树中 R 数据挖掘的空结果
Null Result in R data mining in Decision Tree
我有这个代码
#Import data
tugas=read.csv("D:/FlightDelays.csv")
dipakai=c(1,2,4,8,10,13)
l=dim(tugas)[1]
tugas<-tugas[1:l,dipakai]
## Pembagian Data TRaining dan testtin
n <- round(nrow(tugas)*0.70);n
set.seed(123)
samp=sample(1:nrow(tugas),n)
data.train = tugas[samp,]
data.test = tugas[-samp,]
dim(data.train)
dim(data.test)
fit <- rpart(delay~., data = data.train, method = 'class')
summary(fit)
fit$variable.importance
但是对于 fit$variable.importance
,我不能 运行 结果为空。我该如何解决这个问题?
它不起作用,因为你所有的预测都是多数 class:
fl = https://raw.githubusercontent.com/niharikabalachandra/Logistic-Regression/master/FlightDelays.csv
tugas=read.csv(fl)
dipakai=c(1,2,4,8,10,13)
l=dim(tugas)[1]
tugas<-tugas[1:l,dipakai]
n <- round(nrow(tugas)*0.70)
set.seed(123)
samp=sample(1:nrow(tugas),n)
data.train = tugas[samp,]
data.test = tugas[-samp,]
fit <- rpart(delay~., data = data.train, method = 'class')
table(predict(fit,type="class"))
delayed ontime
0 1541
你需要解决这个学习不平衡的问题..下面我只是调整权重以获得并非全部多数的预测class,但它并没有提高模型的精度:
wt = ifelse(data.train$delay == "delayed",1.5,1)
fit <- rpart(delay~., data = data.train, method = 'class',weights =wt)
table(predict(fit,type="class"))
delayed ontime
97 1444
table(predict(fit,data.train,type="class"),data.train$delay)
delayed ontime
delayed 53 44
ontime 235 1209
你现在可以得到重要性:
fit$variable.importance
carrier dest schedtime dayweek origin
40.275159 23.709600 19.088864 16.221204 9.527087
我有这个代码
#Import data
tugas=read.csv("D:/FlightDelays.csv")
dipakai=c(1,2,4,8,10,13)
l=dim(tugas)[1]
tugas<-tugas[1:l,dipakai]
## Pembagian Data TRaining dan testtin
n <- round(nrow(tugas)*0.70);n
set.seed(123)
samp=sample(1:nrow(tugas),n)
data.train = tugas[samp,]
data.test = tugas[-samp,]
dim(data.train)
dim(data.test)
fit <- rpart(delay~., data = data.train, method = 'class')
summary(fit)
fit$variable.importance
但是对于 fit$variable.importance
,我不能 运行 结果为空。我该如何解决这个问题?
它不起作用,因为你所有的预测都是多数 class:
fl = https://raw.githubusercontent.com/niharikabalachandra/Logistic-Regression/master/FlightDelays.csv
tugas=read.csv(fl)
dipakai=c(1,2,4,8,10,13)
l=dim(tugas)[1]
tugas<-tugas[1:l,dipakai]
n <- round(nrow(tugas)*0.70)
set.seed(123)
samp=sample(1:nrow(tugas),n)
data.train = tugas[samp,]
data.test = tugas[-samp,]
fit <- rpart(delay~., data = data.train, method = 'class')
table(predict(fit,type="class"))
delayed ontime
0 1541
你需要解决这个学习不平衡的问题..下面我只是调整权重以获得并非全部多数的预测class,但它并没有提高模型的精度:
wt = ifelse(data.train$delay == "delayed",1.5,1)
fit <- rpart(delay~., data = data.train, method = 'class',weights =wt)
table(predict(fit,type="class"))
delayed ontime
97 1444
table(predict(fit,data.train,type="class"),data.train$delay)
delayed ontime
delayed 53 44
ontime 235 1209
你现在可以得到重要性:
fit$variable.importance
carrier dest schedtime dayweek origin
40.275159 23.709600 19.088864 16.221204 9.527087