我想使用.将度量标准从RMSE更改为RMSLE
caret library
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给出一些样本数据:
ivar1<-rnorm(500, mean = 3, sd = 1)
ivar2<-rnorm(500, mean = 4, sd = 1)
ivar3<-rnorm(500, mean = 5, sd = 1)
ivar4<-rnorm(500, mean = 4, sd = 1)
dvar<-rpois(500, exp(3+ 0.1*ivar1 - 0.25*ivar2))
data<-data.frame(dvar,ivar4,ivar3,ivar2,ivar1)
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl)
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在这里,我想更改为RMSLE并保持图形的概念
plot <-ggplot(model,type=c("g", "o"), metric="RMSE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])
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use*_*650 11
我不知道如何/如果你可以轻松地将RMSE转换为RMSLE,所以你可以尝试更改控制功能.
看rfFuncs$summary它调用一个函数postResample.这是计算RMSE的地方 - 请查看该部分
mse <- mean((pred - obs)^2)
n <- length(obs)
out <- c(sqrt(mse), resamplCor^2)
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所以你可以修改这个函数来计算RMSLE:
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
}
names(out) <- "RMSLE"
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然后,如果此修改函数已保存在一个被调用的函数中mypostResample,则需要更新rfFuncs$summary.
所以完全:
首先更新摘要函数 - 这将使用RMSLE调用新函数
newSumm <- function (data, lev = NULL, model = NULL)
{
if (is.character(data$obs))
data$obs <- factor(data$obs, levels = lev)
mypostResample(data[, "pred"], data[, "obs"])
}
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然后定义新函数来计算RMSLE
mypostResample <- function (pred, obs)
{
isNA <- is.na(pred)
pred <- pred[!isNA]
obs <- obs[!isNA]
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
names(out) <- "RMSLE"
if (any(is.nan(out)))
out[is.nan(out)] <- NA
out
}
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更新rfFuncs
# keep old settings for future use
oldSumm <- rfFuncs$summary
# update with new function
rfFuncs$summary <- newSumm
ctrl <- rfeControl(functions=rfFuncs,
method="cv",
repeats = 5,
verbose = FALSE,
number=5)
set.seed(1)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl, metric="RMSLE")
# plot
ggplot(model,type=c("g", "o"), metric="RMSLE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])
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