Question

我想创建一个带有神经网络和逻辑回归的循环来比较两者之间的自举MSE。我的问题是我的MSE统计数据的制定使得启动功能不起作用。我继续创建了一些模拟数据来说明问题。如果您熟悉Günther，F。，＆amp; Fritsch，S。（2010），你应该立即认识到编码结构。

variable1 <- rnorm(n = 1000, mean = 26, sd = 5)
variable2 <- rnorm(n = 1000, mean = 600, sd = 100)
variable3 <- rnorm(n = 1000, mean = 115, sd = 15)
 group <- 1 
 weight <- ((.3*(variable1/36))+(.3*(variable2/800))+(.3*(variable3/145)))

pt1 <- cbind (group, variable1, variable2, variable3, weight)

variable1 <- rnorm(n = 1000, mean = 21, sd = 5)
variable2 <- rnorm(n = 1000, mean = 500, sd = 100)
variable3 <- rnorm(n = 1000, mean = 100, sd = 15)
group <- 0
weight <- ((.3*(variable1/36))+(.3*(variable2/800))+(.3*(variable3/145)))

pt2  <- cbind (group, variable1, variable2, variable3, weight)

pt3 <- as.data.frame(rbind (pt1, pt2))

Passing <- rbinom (n = 1000, size = 1, prob = pt3$weight)

Data <- as.data.frame (cbind(pt3, Passing))
View (Data)

data <- Data[, sapply(Data, is.numeric)]
maxValue <- as.numeric(apply (data, 2, max))
minValue <- as.numeric(apply (data, 2, min))

data_scaled <- as.data.frame(scale(data, center = minValue, 
scale = maxValue-minValue))

ind <- sample (1:nrow(data_scaled), 500)
train <- data_scaled[ind,]
test <- data_scaled[-ind,]


model <- glm (formula = 
  Passing ~ variable1 + variable2 + variable3, 
  family = "binomial", 
  data = train)

summary (model)
predicted_model <- predict(model,test)


nueral_model <- neuralnet(formula = 
  Passing ~ variable1 + variable2 + variable3,
  hidden = 3 ,threshold = 0.01, linear.output=FALSE, data= train)

plot (nueral_model)
results <- compute (nueral_model, test[2:4])


results <- results$net.result*(max(data$Passing)-
min(data$Passing))+ min(data$Passing)
Values <- (test$Passing)*(max(data$Passing)- 
min(data$Passing)) + min(data$Passing)

MSE_nueral_model <- sum((results - Values)^2)/nrow(test)
MSE_model <- sum((predicted_model - test$Passing)^2)/nrow(test)
print(paste(MSE_nueral_model,MSE_model))

通常使用循环，我可以使用for命令，但在这种情况下，我在循环神经网络时有点迷失。

非常感谢任何帮助。

使用神经网络和逻辑回归创建一个循环来比较R

0 个答案: