Obtain and format results produced by tuning functions
collect_predictions(x, ...) ## Default S3 method: collect_predictions(x, ...) ## S3 method for class 'tune_results' collect_predictions(x, summarize = FALSE, parameters = NULL, ...) collect_metrics(x, ...) ## S3 method for class 'tune_results' collect_metrics(x, summarize = TRUE, ...) ## S3 method for class 'tune_race' collect_metrics(x, summarize = TRUE, ...) collect_notes(x, ...) ## S3 method for class 'tune_results' collect_notes(x, ...)
The results of
Not currently used.
A logical; should metrics be summarized over resamples
An optional tibble of tuning parameter values that can be
used to filter the predicted values before processing. This tibble should
only have columns for each tuning parameter identifier (e.g.
A tibble. The column names depend on the results and the mode of the model.
collect_predictions(), when unsummarized,
there are columns for each tuning parameter (using the
collect_metrics() also has columns
.estimator. When the
results are summarized, there are columns for
When not summarized, the additional columns for the resampling identifier(s)
collect_predictions(), there are additional columns for the resampling
identifier(s), columns for the predicted values (e.g.,
.pred_class, etc.), and a column for the outcome(s) using the original
column name(s) in the data.
collect_predictions() can summarize the various results over
replicate out-of-sample predictions. For example, when using the bootstrap,
each row in the original training set has multiple holdout predictions
(across assessment sets). To convert these results to a format where every
training set same has a single predicted value, the results are averaged
over replicate predictions.
For regression cases, the numeric predictions are simply averaged. For classification models, the problem is more complex. When class probabilities are used, these are averaged and then re-normalized to make sure that they add to one. If hard class predictions also exist in the data, then these are determined from the summarized probability estimates (so that they match). If only hard class predictions are in the results, then the mode is used to summarize.
collect_notes() returns a tibble with columns for the resampling
indicators, the location (preprocessor, model, etc.), type (error or warning),
and the notes.
data("example_ames_knn") # The parameters for the model: extract_parameter_set_dials(ames_wflow) # Summarized over resamples collect_metrics(ames_grid_search) # Per-resample values collect_metrics(ames_grid_search, summarize = FALSE) # --------------------------------------------------------------------------- library(parsnip) library(rsample) library(dplyr) library(recipes) library(tibble) lm_mod <- linear_reg() %>% set_engine("lm") set.seed(93599150) car_folds <- vfold_cv(mtcars, v = 2, repeats = 3) ctrl <- control_resamples(save_pred = TRUE) spline_rec <- recipe(mpg ~ ., data = mtcars) %>% step_ns(disp, deg_free = tune("df")) grid <- tibble(df = 3:6) resampled <- lm_mod %>% tune_grid(spline_rec, resamples = car_folds, control = ctrl, grid = grid) collect_predictions(resampled) %>% arrange(.row) collect_predictions(resampled, summarize = TRUE) %>% arrange(.row) collect_predictions(resampled, summarize = TRUE, grid[1, ]) %>% arrange(.row)
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