Uniform random variable selection strategy.

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

Usage

dr_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 dr_strategy object.

See also

pprf, dr_noop, pp_pda, sr_mean_of_means

Examples

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

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

# Use with pprf
pprf(Type ~ ., data = iris, dr = dr_uniform(n_vars = 2))
#> 
#> Random Forest of Project-Pursuit Oblique Decision Tree
#> -------------------------------------
#> Tree 1:
#> If ([ 0.01 0 0 0.1 ] * x) < 0.1471151:
#>   Predict: setosa 
#> Else:
#>  If ([ 0.01 0 0 0.24 ] * x) < 0.4446007:
#>    Predict: versicolor 
#>  Else:
#>    Predict: virginica 
#> 
#> Tree 2:
#> If ([ -0.07 0.11 0 0 ] * x) < -0.07210007:
#>  If ([ 0 0.04 -0.12 0 ] * x) < -0.4739328:
#>    Predict: virginica 
#>  Else:
#>    Predict: versicolor 
#> Else:
#>   Predict: setosa 
#> 
#>