dials provides a framework for defining, creating, and
managing tuning parameters for modeling. It contains functions
to create tuning parameter objects (e.g. mtry() or
penalty()) and others for creating tuning grids (e.g.
grid_regular()). There are also functions for generating
random values or specifying a transformation of the parameters.
Author
Maintainer: Hannah Frick hannah@posit.co
Authors:
Max Kuhn max@posit.co
Other contributors:
Posit Software, PBC [copyright holder, funder]
Examples
# Suppose we were tuning a linear regression model that was fit with glmnet
# and there was a predictor that used a spline basis function to enable a
# nonlinear fit. We can use `penalty()` and `mixture()` for the glmnet parts
# and `deg_free()` for the spline.
# A full 3^3 factorial design where the regularization parameter is on
# the log scale:
simple_set <- grid_regular(penalty(), mixture(), deg_free(), levels = 3)
simple_set
#> # A tibble: 27 × 3
#> penalty mixture deg_free
#> <dbl> <dbl> <int>
#> 1 0.0000000001 0 1
#> 2 0.00001 0 1
#> 3 1 0 1
#> 4 0.0000000001 0.5 1
#> 5 0.00001 0.5 1
#> 6 1 0.5 1
#> 7 0.0000000001 1 1
#> 8 0.00001 1 1
#> 9 1 1 1
#> 10 0.0000000001 0 3
#> # ℹ 17 more rows
# A random grid of 5 combinations
set.seed(362)
random_set <- grid_random(penalty(), mixture(), deg_free(), size = 5)
random_set
#> # A tibble: 5 × 3
#> penalty mixture deg_free
#> <dbl> <dbl> <int>
#> 1 0.00137 0.186 1
#> 2 0.0979 0.199 1
#> 3 0.00000251 0.494 1
#> 4 0.00000766 0.790 2
#> 5 0.0000000324 0.251 3
# A small space-filling design based on experimental design methods:
design_set <- grid_space_filling(penalty(), mixture(), deg_free(), size = 5)
design_set
#> # A tibble: 5 × 3
#> penalty mixture deg_free
#> <dbl> <dbl> <int>
#> 1 0.0000000001 0.75 2
#> 2 0.0000000316 0.5 5
#> 3 0.00001 0 1
#> 4 0.00316 1 3
#> 5 1 0.25 4