Introduction

This confusion usually comes from comparing three different things as if they were the same: the lambda values you supplied in caret's tuning grid, the full regularization path stored by the underlying glmnet model, and the single tuning result that caret selected as best. Those values are related, but they do not live in the fitted object in the same way.

What caret Actually Tunes

When you train with method = "glmnet", caret evaluates combinations of alpha and lambda from tuneGrid during resampling.

1library(caret)
2library(glmnet)
3
4set.seed(123)
5x <- matrix(rnorm(200 * 5), ncol = 5)
6y <- rnorm(200)
7
8grid <- expand.grid(
9  alpha = c(0, 1),
10  lambda = c(0.01, 0.1, 1)
11)
12
13fit <- train(
14  x = x,
15  y = y,
16  method = "glmnet",
17  tuneGrid = grid,
18  trControl = trainControl(method = "cv", number = 3)
19)
20
21fit$results
22fit$bestTune

fit$results shows the resampled performance for the grid you asked caret to try. fit$bestTune shows the winning alpha and lambda combination.

If your only question is "which lambda did caret choose," fit$bestTune$lambda is the answer.

Why finalModel$lambda Looks Different

The surprise usually comes from inspecting fit$finalModel$lambda.

fit$finalModel$lambda

That object belongs to the underlying glmnet fit, and glmnet naturally stores a lambda path. It is not simply a copy of the winning value from caret's tuning summary. So seeing more lambda values than expected does not automatically mean caret ignored your grid.

The important distinction is:

• 'fit$bestTune$lambda is the tuning result chosen by caret'
• 'fit$finalModel$lambda is the lambda sequence stored by the fitted glmnet model object'

Those are not interchangeable.

Use the Winning Lambda Explicitly

If you want coefficients or predictions at the selected tuning value, pass that value into the glmnet methods explicitly.

coef(fit$finalModel, s = fit$bestTune$lambda)

You can do the same when generating predictions directly from the final model.

predict(fit$finalModel, newx = x[1:5, ], s = fit$bestTune$lambda)

This is the practical way to connect the caret tuning result to the stored glmnet object.

Remember That caret Refits the Final Model

After tuning, caret typically refits the final model on the full training data using the selected tuning parameters. That final object is not just a raw copy of one fold from the resampling process.

Because of that refit step, the internal structure of fit$finalModel can look different from what you expected from the resampling grid alone. The key is not to treat the internal lambda path as if it were the sole record of caret's tuning decision.

Inspect the Right Part of the Object

A reliable inspection workflow is:

1. look at fit$results to confirm the evaluated grid
2. look at fit$bestTune to see the chosen parameters
3. use coef(..., s = fit$bestTune$lambda) or matching prediction calls when working with the stored glmnet object

That keeps the role of each object clear and avoids reading too much meaning into finalModel$lambda.

When Direct glmnet May Be Simpler

If you need exact control over the lambda sequence, exact storage shape, or every detail of the fitting path, using glmnet directly can be clearer than going through caret.

1library(glmnet)
2
3fit_glmnet <- glmnet(x, y, alpha = 1, lambda = c(0.01, 0.1, 1))
4print(fit_glmnet$lambda)

caret is useful for resampling and model comparison, but that abstraction means the final object is not always best interpreted the same way you would interpret a hand-built glmnet fit.

Common Pitfalls

A common mistake is assuming fit$finalModel$lambda must equal the one chosen tuning value. In practice, it represents the lambda path stored in the glmnet model object.

Another is forgetting that fit$bestTune already exposes the selected hyperparameters clearly. Many debugging sessions start by reading the wrong slot.

It is also easy to overlook the final refit step and compare the post-tuning model too literally against fold-level expectations.

Summary

• 'tuneGrid defines what caret evaluates during resampling.'
• 'fit$bestTune$lambda is the selected tuning result.'
• 'fit$finalModel$lambda is the lambda path stored by the underlying glmnet fit.'
• Use s = fit$bestTune$lambda when asking the final model for coefficients or predictions.
• If you need full control of the lambda path, consider using glmnet directly instead of caret.