Context: I try to evaluate a model, made using tune::last_fit() with an independent dataset.
Problem: it seems that the metrics obtained with tune::collect_metrics() are different from the ones obtained using summary().
Question: what is the difference between the metric (here the R²) calculated using tune::collect_metrics() and summary()? Which one corresponds to the R² between observation from the independent dataset and predictions of these observations?
Reproducible example: using the example from https://tune.tidymodels.org/reference/last_fit.html as a starting point.
library(recipes)
library(rsample)
library(parsnip)
set.seed(6735)
tr_te_split <- initial_split(mtcars)
spline_rec <- recipe(mpg ~ ., data = mtcars) %>%
step_ns(disp)
lin_mod <- linear_reg() %>%
set_engine("lm")
spline_res <- tune::last_fit(lin_mod, spline_rec, split = tr_te_split)
spline_res
#> # Resampling results
#> # Manual resampling
#> # A tibble: 1 × 6
#> splits id .metrics .notes .predictions .workflow
#> <list> <chr> <list> <list> <list> <list>
#> 1 <split [24/8]> train/test split <tibble> <tibble> <tibble [8 × 4]> <workflow>
# Here are the performance metrics for the model
tune::collect_metrics(spline_res)
#> # A tibble: 2 × 4
#> .metric .estimator .estimate .config
#> <chr> <chr> <dbl> <chr>
#> 1 rmse standard 3.80 Preprocessor1_Model1
#> 2 rsq standard 0.729 Preprocessor1_Model1
spline_res %>%
parsnip::extract_fit_engine() %>% # back to stats lm object
summary()
#>
#> Call:
#> stats::lm(formula = ..y ~ ., data = data)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.4453 -1.1980 -0.1464 1.3246 2.8223
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 23.087028 18.641785 1.238 0.239
#> cyl 0.326218 1.402236 0.233 0.820
#> hp 0.005969 0.024848 0.240 0.814
#> drat -0.009576 1.597293 -0.006 0.995
#> wt -0.902839 2.503336 -0.361 0.725
#> qsec 0.185826 0.745021 0.249 0.807
#> vs 1.492756 2.255781 0.662 0.521
#> am 4.101555 3.110797 1.318 0.212
#> gear 0.174875 1.730223 0.101 0.921
#> carb -1.278962 1.009824 -1.267 0.229
#> disp_ns_1 -15.149506 13.649995 -1.110 0.289
#> disp_ns_2 -4.905087 6.756046 -0.726 0.482
#>
#> Residual standard error: 2.397 on 12 degrees of freedom
#> Multiple R-squared: 0.9204, Adjusted R-squared: 0.8473
#> F-statistic: 12.61 on 11 and 12 DF, p-value: 5.869e-05
Created on 2023-05-22 with reprex v2.0.2
As you can see, both R² are not equal.
The statistics that you get via last_fit() are from holdout data. The ones from summary.lm() are not; they are from the same data being used to fit the model.
The re-use of data to assess model performance is a major pitfall when modeling. It will give you optimistic results (perhaps overwhelmingly optimistic, depending on the model).
There are tons of references on this. We give a small example in the tdiymodels book.
Also, while this is not the issue, tidymodels (and caret before it) use a different estimator for $R^2$ than the canonical one used by linear regression (see ?yardstick::rsq). It performs better when the models have metrics closer to zero.