Introduction

In caret, the message "Something is wrong; all the RMSE metric values are missing" usually means the resampling runs failed to produce valid numeric predictions for at least one fold, so train() could not compute the requested regression metric. The message itself is generic. The real cause is usually in the warnings that came before it: missing values, wrong outcome type, failed model fitting, or bad resampling splits.

What RMSE Requires

RMSE is a regression metric. To calculate it, caret needs:

• a numeric outcome variable
• successful model fits on resamples
• numeric predictions for the holdout portion of each resample
• no NA, NaN, or Inf values contaminating the predictions or outcome

If any of those conditions fail across the resamples, RMSE ends up missing.

The Most Common Causes

Typical root causes include:

• the outcome is a factor, but you are running a regression workflow
• predictor columns contain missing values that the model method cannot handle
• preprocessing creates invalid values in some folds
• the model fails to converge or errors inside resampling
• a fold has too little variation for the chosen model

A simple check is:

str(training_data)
summary(training_data)

If the target column is a factor and you expected regression, that alone explains the RMSE problem.

Check the Outcome Type First

For regression, the target must be numeric.

is.numeric(training_data$y)

If it is not numeric, fix that before calling train().

training_data$y <- as.numeric(training_data$y)

Be careful with factor-to-numeric conversion in R. If the variable is a factor, convert through character when the values represent numbers:

training_data$y <- as.numeric(as.character(training_data$y))

Direct as.numeric(factor_var) may give integer level codes instead of the intended values.

Inspect Missing or Invalid Values

A model can fail silently inside resampling because one fold contains missing or infinite values.

colSums(is.na(training_data))
colSums(!is.finite(as.matrix(training_data[sapply(training_data, is.numeric)])))

If missing values are present, either clean them first or use a preprocessing strategy.

1library(caret)
2
3ctrl <- trainControl(method = "cv", number = 5)
4model <- train(
5  y ~ .,
6  data = training_data,
7  method = "lm",
8  trControl = ctrl,
9  preProcess = c("medianImpute")
10)

That does not solve every data issue, but it handles a common one.

Read the Warnings, Not Just the Final Error

caret usually prints warnings during resampling that explain why metrics are missing. Those messages are often much more informative than the final summary error.

A useful debugging pattern is to try a simpler model first:

1model <- train(
2  y ~ .,
3  data = training_data,
4  method = "lm",
5  trControl = trainControl(method = "cv", number = 3)
6)

If even a plain linear model fails, the problem is likely in the data or the resampling setup rather than in an advanced model's tuning grid.

Verify the Metric Matches the Problem Type

If you accidentally ask for RMSE while using a classification target, the workflow is misaligned.

For classification, the target is usually a factor and metrics like Accuracy, ROC, Kappa, or log loss are more appropriate.

For regression, the target should be numeric and RMSE or MAE make sense.

This split is basic, but it is one of the most common reasons for cryptic caret metric failures.

Resampling Edge Cases

Some datasets are small enough or strange enough that certain folds become problematic. Examples:

• a predictor has near-zero variance in a fold
• factor levels disappear in some resamples
• a transformation behaves badly on a tiny subset

You can simplify the resampling temporarily to diagnose:

ctrl <- trainControl(method = "cv", number = 3)

If fewer folds work and more folds fail, you may have a fold-fragility issue rather than a global modeling problem.

Common Pitfalls

The most common mistake is using a factor outcome while expecting a regression metric such as RMSE.

Another mistake is ignoring warnings emitted during model fitting and focusing only on the final summary message. The useful information is usually earlier in the output.

Developers also often assume the model method is broken when the real issue is NA, Inf, or invalid type conversion in the training data.

Finally, if a complex model fails, try a simpler one first. That narrows the problem much faster than tweaking many tuning parameters blindly.

Summary

• Missing RMSE values usually mean resampling failed to produce valid numeric predictions.
• Check that the outcome is numeric if you are doing regression.
• Inspect NA, NaN, and Inf values in both predictors and target.
• Read the warnings produced during train(), not just the final error line.
• Use a simpler model and simpler resampling setup to isolate the root cause quickly.