Uniform random variable selection strategy.

Creates a variable selection strategy that randomly selects a subset of variables at each split. Used with pprf for random forests.

Usage

vars_uniform(n_vars = NULL, p_vars = NULL)

Arguments

n_vars

The number of variables to consider at each split (integer). Cannot be used together with p_vars.

p_vars

The proportion of variables to consider at each split (number between 0 and 1, exclusive). Resolved to an integer count when the number of features is known. Cannot be used together with n_vars.

Value

A vars_strategy object.

Details

Exactly one of n_vars or p_vars may be specified. When p_vars is used, it is stored as-is and resolved to an integer count later by pprf once the number of features is known.

See also

pprf, vars_all, pp_pda, cutpoint_mean_of_means

Examples

# Select 2 variables at each split
vars_uniform(n_vars = 2)
#> $name
#> [1] "uniform"
#> 
#> $display_name
#> [1] "Uniform random"
#> 
#> $count
#> [1] 2
#> 
#> $p_vars
#> NULL
#> 
#> attr(,"class")
#> [1] "vars_strategy"

# Select half the variables at each split
vars_uniform(p_vars = 0.5)
#> $name
#> [1] "uniform"
#> 
#> $display_name
#> [1] "Uniform random"
#> 
#> $count
#> NULL
#> 
#> $p_vars
#> [1] 0.5
#> 
#> attr(,"class")
#> [1] "vars_strategy"

# Use with pprf
pprf(Species ~ ., data = iris, vars = vars_uniform(n_vars = 2))
#> 
#> Random Forest of Projection-Pursuit Oblique Decision Trees
#>   Call:        pprf(formula = Species ~ ., data = iris, vars = vars_uniform(n_vars = 2))
#>   Trees:       100
#>   Mode:        classification
#>   Group names: setosa, versicolor, virginica
#>   Formula:     Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width -     1
#>