相关问题-1
我有一个这样的数据集：
> head(training_data)
year month channelGrouping visitStartTime visitNumber timeSinceLastVisit browser
1 2016 October Social 1477775021 1 0 Chrome
2 2016 September Social 1473037945 1 0 Safari
3 2017 July Organic Search 1500305542 1 0 Chrome
4 2017 July Organic Search 1500322111 2 16569 Chrome
5 2016 August Social 1471890172 1 0 Safari
6 2017 May Direct 1495146428 1 0 Chrome
operatingSystem isMobile continent subContinent country source medium
1 Windows 0 Americas South America Brazil youtube.com referral
2 Macintosh 0 Americas Northern America United States youtube.com referral
3 Windows 0 Americas Northern America Canada google organic
4 Windows 0 Americas Northern America Canada google organic
5 Macintosh 0 Africa Eastern Africa Zambia youtube.com referral
6 Android 1 Americas Northern America United States (direct)
isTrueDirect hits pageviews positiveTransaction
1 0 1 1 No
2 0 1 1 No
3 0 5 5 No
4 1 3 3 No
5 0 1 1 No
6 1 6 6 No
> str(training_data)
'data.frame': 1000 obs. of 18 variables:
$ year : int 2016 2016 2017 2017 2016 2017 2016 2017 2017 2016 ...
$ month : Factor w/ 12 levels "January","February",..: 10 9 7 7 8 5 10 3 3 12 ...
$ channelGrouping : chr "Social" "Social" "Organic Search" "Organic Search" ...
$ visitStartTime : int 1477775021 1473037945 1500305542 1500322111 1471890172 1495146428 1476003570 1488556031 1490323225 1480696262 ...
$ visitNumber : int 1 1 1 2 1 1 1 1 1 1 ...
$ timeSinceLastVisit : int 0 0 0 16569 0 0 0 0 0 0 ...
$ browser : chr "Chrome" "Safari" "Chrome" "Chrome" ...
$ operatingSystem : chr "Windows" "Macintosh" "Windows" "Windows" ...
$ isMobile : int 0 0 0 0 0 1 0 1 0 0 ...
$ continent : Factor w/ 5 levels "Africa","Americas",..: 2 2 2 2 1 2 3 3 2 4 ...
$ subContinent : chr "South America" "Northern America" "Northern America" "Northern America" ...
$ country : chr "Brazil" "United States" "Canada" "Canada" ...
$ source : chr "youtube.com" "youtube.com" "google" "google" ...
$ medium : chr "referral" "referral" "organic" "organic" ...
$ isTrueDirect : int 0 0 0 1 0 1 0 0 0 0 ...
$ hits : int 1 1 5 3 1 6 1 1 2 1 ...
$ pageviews : int 1 1 5 3 1 6 1 1 2 1 ...
$ positiveTransaction: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 …
然后，我使用Metrics包定义我的自定义RMSLE函数：
rmsleMetric <- function(data, lev = NULL, model = NULL){
out <- Metrics::rmsle(data$obs, data$pred)
names(out) <- c("rmsle")
return (out)
}
然后，我定义了trainControl:
tc <- trainControl(method = "repeatedcv",
number = 5,
repeats = 5,
summaryFunction = rmsleMetric,
classProbs = TRUE)
我的网格搜索：
tg <- expand.grid(alpha = 0, lambda = seq(0, 1, by = 0.1))
最后，我的模型：
penalizedLogit_ridge <- train(positiveTransaction ~ .,
data = training_data,
metric="rmsle",
method = "glmnet",
family = "binomial",
trControl = tc,
tuneGrid = tg
)
当我尝试运行上面的命令时，我得到一个错误：
Something is wrong; all the rmsle metric values are missing:
rmsle
Min. : NA
1st Qu.: NA
Median : NA
Mean :NaN
3rd Qu.: NA
Max. : NA
NA's :11
Error: Stopping
In addition: There were 50 or more warnings (use warnings() to see the first 50)
查看警告，我发现：
1: In Ops.factor(1, actual) : ‘+’ not meaningful for factors
2: In Ops.factor(1, predicted) : ‘+’ not meaningful for factors
重复25次
由于如果我使用prSummary作为汇总函数将度量更改为AUC，同样的事情也会发生，所以我不认为我的数据有任何问题。
所以，我相信我的函数是错误的，但我不知道如何弄清楚为什么它是错误的。
非常感谢您的帮助。
您的自定义度量没有正确定义。如果您将classProbs = TRUE和savePredictions = "final"与trainControl一起使用，您会意识到有两列根据您的目标类别命名，其中包含预测概率，而data$pred列包含无法用于计算所需度量的预测类别。
定义函数的正确方法是获得可能的级别，并使用它们来提取其中一类的概率：
rmsleMetric <- function(data, lev = NULL, model = NULL){
lvls <- levels(data$obs)
out <- Metrics::rmsle(ifelse(data$obs == lev[2], 0, 1),
data[, lvls[1]])
names(out) <- c("rmsle")
return (out)
}
它工作吗：
library(caret)
library(mlbench)
data(Sonar)
tc <- trainControl(method = "repeatedcv",
number = 2,
repeats = 2,
summaryFunction = rmsleMetric,
classProbs = TRUE,
savePredictions = "final")
tg <- expand.grid(alpha = 0, lambda = seq(0, 1, by = 0.1))
penalizedLogit_ridge <- train(Class ~ .,
data = Sonar,
metric="rmsle",
method = "glmnet",
family = "binomial",
trControl = tc,
tuneGrid = tg)
#output
glmnet
208 samples
60 predictor
2 classes: 'M', 'R'
No pre-processing
Resampling: Cross-Validated (2 fold, repeated 2 times)
Summary of sample sizes: 105, 103, 104, 104
Resampling results across tuning parameters:
lambda rmsle
0.0 0.2835407
0.1 0.2753197
0.2 0.2768288
0.3 0.2797847
0.4 0.2827953
0.5 0.2856088
0.6 0.2881894
0.7 0.2905501
0.8 0.2927171
0.9 0.2947169
1.0 0.2965505
Tuning parameter 'alpha' was held constant at a value of 0
rmsle was used to select the optimal model using the largest value.
The final values used for the model were alpha = 0 and lambda = 1.
您可以检查caret::twoClassSummary——它的定义非常相似。