插入符号:重新采样的性能度量中存在缺失值 [英] Caret: There were missing values in resampled performance measures
问题描述
我在 Bike Sharing 数据集上运行 caret 的神经网络，我收到以下错误消息:
I am running caret's neural network on the Bike Sharing dataset and I get the following error message:
在nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,:重新采样的绩效指标中存在缺失值.
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
我不确定是什么问题.有人可以帮忙吗?
I am not sure what the problem is. Can anyone help please?
数据集来自:https://archive.ics.uci.edu/ml/datasets/自行车+共享+数据集
代码如下:
library(caret)
library(bestNormalize)
data_hour = read.csv("hour.csv")
# Split dataset
set.seed(3)
split = createDataPartition(data_hour$casual, p=0.80, list=FALSE)
validation = data_hour[-split,]
dataset = data_hour[split,]
dataset = dataset[,c(-1,-2,-4)]
# View strucutre of data
str(dataset)
# 'data.frame': 13905 obs. of 14 variables:
# $ season : int 1 1 1 1 1 1 1 1 1 1 ...
# $ mnth : int 1 1 1 1 1 1 1 1 1 1 ...
# $ hr : int 1 2 3 5 8 10 11 12 14 15 ...
# $ holiday : int 0 0 0 0 0 0 0 0 0 0 ...
# $ weekday : int 6 6 6 6 6 6 6 6 6 6 ...
# $ workingday: int 0 0 0 0 0 0 0 0 0 0 ...
# $ weathersit: int 1 1 1 2 1 1 1 1 2 2 ...
# $ temp : num 0.22 0.22 0.24 0.24 0.24 0.38 0.36 0.42 0.46 0.44 ...
# $ atemp : num 0.273 0.273 0.288 0.258 0.288 ...
# $ hum : num 0.8 0.8 0.75 0.75 0.75 0.76 0.81 0.77 0.72 0.77 ...
# $ windspeed : num 0 0 0 0.0896 0 ...
# $ casual : int 8 5 3 0 1 12 26 29 35 40 ...
# $ registered: int 32 27 10 1 7 24 30 55 71 70 ...
# $ cnt : int 40 32 13 1 8 36 56 84 106 110 ...
## transform numeric data to Guassian
dataset_selected = dataset[,c(-13,-14)]
for (i in 8:12) { dataset_selected[,i] = predict(boxcox(dataset_selected[,i] +0.1))}
# View transformed dataset
str(dataset_selected)
#'data.frame': 13905 obs. of 12 variables:
#' $ season : int 1 1 1 1 1 1 1 1 1 1 ...
#' $ mnth : int 1 1 1 1 1 1 1 1 1 1 ...
#' $ hr : int 1 2 3 5 8 10 11 12 14 15 ...
#' $ holiday : int 0 0 0 0 0 0 0 0 0 0 ...
#' $ weekday : int 6 6 6 6 6 6 6 6 6 6 ...
#' $ workingday: int 0 0 0 0 0 0 0 0 0 0 ...
#' $ weathersit: int 1 1 1 2 1 1 1 1 2 2 ...
#' $ temp : num -1.47 -1.47 -1.35 -1.35 -1.35 ...
#' $ atemp : num -1.18 -1.18 -1.09 -1.27 -1.09 ...
#' $ hum : num 0.899 0.899 0.637 0.637 0.637 ...
#' $ windspeed : num -1.8 -1.8 -1.8 -0.787 -1.8 ...
#' $ casual : num -0.361 -0.588 -0.81 -1.867 -1.208 ...
# Train data with Neural Network model from caret
control = trainControl(method = 'repeatedcv', number = 10, repeats =3)
metric = 'RMSE'
set.seed(3)
fit = train(casual ~., data = dataset_selected, method = 'nnet', metric = metric, trControl = control, trace = FALSE)
感谢您的帮助！
推荐答案
phivers 的评论是正确的，但是我仍然想针对这个具体示例提供更详细的答案.
phivers comment is spot on, however I would still like to provide a more verbose answer on this concrete example.
为了更详细地调查正在发生的事情，应该将参数 savePredictions = "all" 添加到 trainControl:
In order to investigate what is going on in more detail one should add the argument savePredictions = "all" to trainControl:
control = trainControl(method = 'repeatedcv',
number = 10,
repeats = 3,
returnResamp = "all",
savePredictions = "all")
metric = 'RMSE'
set.seed(3)
fit = train(casual ~.,
data = dataset_selected,
method = 'nnet',
metric = metric,
trControl = control,
trace = FALSE,
form = "traditional")
现在运行时:
fit$results
#output
size decay RMSE Rsquared MAE RMSESD RsquaredSD MAESD
1 1 0e+00 0.9999205 NaN 0.8213177 0.009655872 NA 0.007919575
2 1 1e-04 0.9479487 0.1850270 0.7657225 0.074211541 0.20380571 0.079640883
3 1 1e-01 0.8801701 0.3516646 0.6937938 0.074484860 0.20787440 0.077960642
4 3 0e+00 0.9999205 NaN 0.8213177 0.009655872 NA 0.007919575
5 3 1e-04 0.9272942 0.2482794 0.7434689 0.091409600 0.24363651 0.098854133
6 3 1e-01 0.7943899 0.6193242 0.5944279 0.011560524 0.03299137 0.013002708
7 5 0e+00 0.9999205 NaN 0.8213177 0.009655872 NA 0.007919575
8 5 1e-04 0.8811411 0.3621494 0.6941335 0.092169810 0.22980560 0.098987058
9 5 1e-01 0.7896507 0.6431808 0.5870894 0.009947324 0.01063359 0.009121535
我们注意到当decay = 0时出现问题.
we notice the problem occurs when decay = 0.
让我们过滤 decay = 0
library(tidyverse)
fit$pred %>%
filter(decay == 0) -> for_r2
var(for_r2$pred)
#output
0
我们可以观察到 decay == 0 时的所有预测都是相同的(方差为零).该模型仅预测 0:
we can observe that all of the predictions when decay == 0 are the same (have zero variance). The model exclusively predicts 0:
unique(for_r2$pred)
#output
0
所以当汇总函数试图预测 R 平方时:
So when the summary function tries to predict R squared:
caret::R2(for_r2$obs, for_r2$pred)
#output
[1] NA
Warning message:
In cor(obs, pred, use = ifelse(na.rm, "complete.obs", "everything")) :
the standard deviation is zero
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