2
DNN用的是h2o.deeplearning
功能来执行。h2o.deeplearning预测误差中的R
最后,我使用的h2o.predict
函数来执行测试数据预测。
但是当我尝试在可视化的方式来显示实际值与预测值,我得到了一个错误。这里是我的代码:
library("h2o")
h2o.init(nthreads = -1, max_mem_size = "5G")
credit<-read.csv("http://freakonometrics.free.fr/german_credit.csv", header=TRUE)
F=c(1,2,4,5,7,8,9,10,11,12,13,15,16,17,18,19,20,21)
for(i in F) credit[,i]=as.factor(credit[,i])
str(credit)
library(caret)
set.seed(1000)
intrain<-createDataPartition(y=credit$Creditability, p=0.7, list=FALSE)
train<-credit[intrain, ]
test<-credit[-intrain, ]
deep_train<-as.h2o(train,destination_frame = "deep_train")
deep_test<-as.h2o(test,destination_frame = "deep_test")
h2o.str(deep_train)
h2o.str(deep_test)
x<-names(train[,-1])
y<-"Creditability"
deep_model<-h2o.deeplearning(x=x, y=y,
training_frame = deep_train,
activation = "RectifierWithDropout",
hidden=c(30,40,50),
epochs = 10,
input_dropout_ratio = 0.2,
hidden_dropout_ratios = c(0.5,0.5,0.5),
l1=1e-5 ,l2= 0,
rho = 0.99, epsilon = 1e-08,
loss = "CrossEntropy",
variable_importances = TRUE)
pred<-h2o.predict(deep_model, newdata=deep_test)
confusionMatrix(pred$predict, test$Creditability)
Error in unique.default(x, nmax = nmax) :
invalid type/length (environment/0) in vector allocation
如何可视化预测表?
谢谢。但如何使用h2o.confusionMatrix? ? ? –
请参阅该函数的R文件:'h2o.confusionMatrix(deep_model,newdata = deep_test)' –