Error when using (caret) to train an mlp model - r

Question

I used caret to train an mlp model with this code.
library(datasets)
library(MASS)
library(caret)
DP = caret::createDataPartition(Boston$medv, p=0.75, list = F)
train = Boston[DP,]
test = Boston[-DP,]
colnames(train) = colnames(Boston)
colnames(test) = colnames(Boston)
mlp = caret::train(medv ~., data = Boston, method = "mlp", trControl = trainControl(method = "cv", number = 3),
tuneGrid = expand.grid(size = 1:3), linOut = T, metric = "RMSE")
Yp = caret::predict.train(mlp, test[,1:13])
I got this error message:
In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : There were missing values in resampled performance measures.
please guys I need to understand why I got this error?

Answer 1

The R squared value for some runs throws NA, you can check the output:
set.seed(111)
mlp = caret::train(medv ~., data = Boston, method = "mlp",
trControl = trainControl(method = "cv", number = 3),
tuneGrid = expand.grid(size = 1:3), linOut = T, metric = "RMSE")
mlp$results
size RMSE Rsquared MAE RMSESD RsquaredSD MAESD
1 1 9.152376 NaN 6.640184 0.9213123 NA 0.6877405
2 2 14.353732 0.000965434 12.274448 8.3227673 NA 8.6994894
3 3 12.701064 NaN 10.988850 3.2658958 NA 3.6549478
Note even for models that work, your Rsquared is too low. Two problems with the model, 1) your size might be too low, and 2) you don't scale the data, so your predictions give you only one value, and R2 will be total nonsense:
Yp = caret::predict.train(mlp, test[,1:13])
table(Yp)
Yp
20.0358009338379
125
Try something like this:
mlp = caret::train(medv ~., data = Boston, method = "mlp",
trControl = trainControl(method = "cv", number = 3),
preProcess = c("center","scale"),
tuneGrid = expand.grid(size = 3:5), linOut = T, metric = "RMSE")
mlp
Multi-Layer Perceptron
506 samples
13 predictor
Pre-processing: centered (13), scaled (13)
Resampling: Cross-Validated (3 fold)
Summary of sample sizes: 337, 338, 337
Resampling results across tuning parameters:
size RMSE Rsquared MAE
3 7.926669 0.3291762 5.619198
4 6.976707 0.4913297 5.130273
5 6.894459 0.5188481 5.040821