The important thing to realize about `fit_resamples()`

is that its purpose is to **measure performance**. The models that you train in `fit_resamples()`

are not kept or used later.

Let's imagine that you know the parameters you want to use for an SVM model.

```
library(tidymodels)
#> ── Attaching packages ─────────────────────────── tidymodels 0.1.1 ──
#> ✓ broom 0.7.0 ✓ recipes 0.1.13
#> ✓ dials 0.0.8 ✓ rsample 0.0.7
#> ✓ dplyr 1.0.0 ✓ tibble 3.0.3
#> ✓ ggplot2 3.3.2 ✓ tidyr 1.1.0
#> ✓ infer 0.5.3 ✓ tune 0.1.1
#> ✓ modeldata 0.0.2 ✓ workflows 0.1.2
#> ✓ parsnip 0.1.2 ✓ yardstick 0.0.7
#> ✓ purrr 0.3.4
#> ── Conflicts ────────────────────────────── tidymodels_conflicts() ──
#> x purrr::discard() masks scales::discard()
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x recipes::step() masks stats::step()
## pretend this is your training data
data("hpc_data")
svm_spec <- svm_poly(degree = 1, cost = 1/4) %>%
set_engine("kernlab") %>%
set_mode("regression")
svm_wf <- workflow() %>%
add_model(svm_spec) %>%
add_formula(compounds ~ .)
hpc_folds <- vfold_cv(hpc_data)
svm_rs <- svm_wf %>%
fit_resamples(
resamples = hpc_folds
)
svm_rs
#> # Resampling results
#> # 10-fold cross-validation
#> # A tibble: 10 x 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [3.9K/434]> Fold01 <tibble [2 × 3]> <tibble [0 × 1]>
#> 2 <split [3.9K/433]> Fold02 <tibble [2 × 3]> <tibble [0 × 1]>
#> 3 <split [3.9K/433]> Fold03 <tibble [2 × 3]> <tibble [0 × 1]>
#> 4 <split [3.9K/433]> Fold04 <tibble [2 × 3]> <tibble [0 × 1]>
#> 5 <split [3.9K/433]> Fold05 <tibble [2 × 3]> <tibble [0 × 1]>
#> 6 <split [3.9K/433]> Fold06 <tibble [2 × 3]> <tibble [0 × 1]>
#> 7 <split [3.9K/433]> Fold07 <tibble [2 × 3]> <tibble [0 × 1]>
#> 8 <split [3.9K/433]> Fold08 <tibble [2 × 3]> <tibble [0 × 1]>
#> 9 <split [3.9K/433]> Fold09 <tibble [2 × 3]> <tibble [0 × 1]>
#> 10 <split [3.9K/433]> Fold10 <tibble [2 × 3]> <tibble [0 × 1]>
```

There are no fitted models in this output. Models were fitted to each of these resamples, but you don't want to use them for anything; they are thrown away because their only purpose is for computing the `.metrics`

to estimate performance.

If you want a model to use to predict on new data, you need to go back to your whole training set and fit your model once again, with the entire training set.

```
svm_fit <- svm_wf %>%
fit(hpc_data)
svm_fit
#> ══ Workflow [trained] ═══════════════════════════════════════════════
#> Preprocessor: Formula
#> Model: svm_poly()
#>
#> ── Preprocessor ─────────────────────────────────────────────────────
#> compounds ~ .
#>
#> ── Model ────────────────────────────────────────────────────────────
#> Support Vector Machine object of class "ksvm"
#>
#> SV type: eps-svr (regression)
#> parameter : epsilon = 0.1 cost C = 0.25
#>
#> Polynomial kernel function.
#> Hyperparameters : degree = 1 scale = 1 offset = 1
#>
#> Number of Support Vectors : 2827
#>
#> Objective Function Value : -284.7255
#> Training error : 0.835421
```

^{Created on 2020-07-17 by the reprex package (v0.3.0)}

This final object is one that you can use with `pull_workflow_fit()`

for variable importance or similar.