Fit regression random forest to klassets_xy object

Fit regression random forest to klassets_xy object

Usage

fit_regression_random_forest(
  df,
  ntree = 500L,
  maxdepth = Inf,
  trace = FALSE,
  ...
)

Arguments

df

A object from sim_xy.

ntree

Number of trees to grow for the forest.

maxdepth

Max depth of each trees.

trace

A logical indicating if a progress bar shall be printed while the forest grows.

...

Options for ranger::ranger.

Examples

df <- sim_xy()

df
#> # A tibble: 500 × 2
#>        x     y
#>    <dbl> <dbl>
#>  1  1.87  3.22
#>  2  2.16  4.59
#>  3  2.32  4.50
#>  4  2.34  4.02
#>  5  2.39  4.19
#>  6  2.61  4.55
#>  7  2.68  4.67
#>  8  2.78  3.53
#>  9  2.80  5.13
#> 10  2.94  3.77
#> # … with 490 more rows

dfrrf <- fit_regression_random_forest(df)

dfrrf
#> # A tibble: 500 × 3
#>        x     y prediction
#>    <dbl> <dbl>      <dbl>
#>  1  1.87  3.22       3.71
#>  2  2.16  4.59       4.39
#>  3  2.32  4.50       4.38
#>  4  2.34  4.02       4.30
#>  5  2.39  4.19       4.30
#>  6  2.61  4.55       4.49
#>  7  2.68  4.67       4.49
#>  8  2.78  3.53       4.09
#>  9  2.80  5.13       4.61
#> 10  2.94  3.77       3.85
#> # … with 490 more rows

plot(dfrrf)


df <- sim_xy(n = 1000, x_dist = runif)
df <- dplyr::mutate(df, y = y + 2*sin(5 * x))
plot(df)


plot(fit_regression_random_forest(df))

plot(fit_regression_random_forest(df, ntree = 100, maxdepth = 3))