For some tuning parameters, the range of values depend on the data dimensions (e.g. mtry). Some packages will fail if the parameter values are outside of these ranges. Since the model might receive resampled versions of the data, these ranges can't be set prior to the point where the model is fit. These functions check the possible range of the data and adjust them if needed (with a warning).

min_cols(num_cols, source)

min_rows(num_rows, source, offset = 0)

## Arguments

num_cols, num_rows The parameter value requested by the user. A data frame for the data to be used in the fit. If the source is named "data", it is assumed that one column of the data corresponds to an outcome (and is subtracted off). A number subtracted off of the number of rows available in the data.

## Value

An integer (and perhaps a warning).

## Examples

nearest_neighbor(neighbors= 100) %>%
set_engine("kknn") %>%
set_mode("regression") %>%
translate()
#> K-Nearest Neighbor Model Specification (regression)
#>
#> Main Arguments:
#>   neighbors = 100
#>
#> Computational engine: kknn
#>
#> Model fit template:
#> kknn::train.kknn(formula = missing_arg(), data = missing_arg(),
#>     ks = min_rows(100, data, 5))
library(ranger)
rand_forest(mtry = 2, min_n = 100, trees = 3) %>%
set_engine("ranger") %>%
set_mode("regression") %>%
fit(mpg ~ ., data = mtcars)
#> Warning: 100 samples were requested but there were 32 rows in the data. 32 will be used.#> parsnip model object
#>
#> Fit time:  8ms
#> Ranger result
#>
#> Call:
#>  ranger::ranger(x = maybe_data_frame(x), y = y, mtry = min_cols(~2,      x), num.trees = ~3, min.node.size = min_rows(~100, x), num.threads = 1,      verbose = FALSE, seed = sample.int(10^5, 1))
#>
#> Type:                             Regression
#> Number of trees:                  3
#> Sample size:                      32
#> Number of independent variables:  10
#> Mtry:                             2
#> Target node size:                 32
#> Variable importance mode:         none
#> Splitrule:                        variance
#> OOB prediction error (MSE):       23.6938
#> R squared (OOB):                  0.3477114