This function visualizes the contribution of a base learner to the overall prediction score. For visualization of partial effects see plotPEUni.

plotBaselearner(cboost, blname, npoints = 100L)

Arguments

cboost

[Compboost class]
A trained Compboost object.

blname

[character(1L)]
Name of the base learner. Must be one of cboost$getBaselearnerNames().

npoints

[integer(1L)]
Number of points which are predicted for the lines (only applies to numerical features).

Value

ggplot object containing the graphic.

Examples

cboost = Compboost$new(data = iris, target = "Petal.Length",
  loss = LossQuadratic$new())
cboost$addComponents("Sepal.Width")
cboost$train(500L)
#>   1/500   risk = 1.5  
#>  12/500   risk = 1.3  
#>  24/500   risk = 1.2  
#>  36/500   risk = 1.2  
#>  48/500   risk = 1.1  
#>  60/500   risk = 1.1  
#>  72/500   risk = 1.1  
#>  84/500   risk = 1.1  
#>  96/500   risk = 1.1  
#> 108/500   risk = 1.1  
#> 120/500   risk = 1.1  
#> 132/500   risk = 1.1  
#> 144/500   risk = 1.1  
#> 156/500   risk = 1.1  
#> 168/500   risk = 1.1  
#> 180/500   risk = 1.1  
#> 192/500   risk = 1  
#> 204/500   risk = 1  
#> 216/500   risk = 1  
#> 228/500   risk = 1  
#> 240/500   risk = 1  
#> 252/500   risk = 1  
#> 264/500   risk = 1  
#> 276/500   risk = 1  
#> 288/500   risk = 1  
#> 300/500   risk = 1  
#> 312/500   risk = 1  
#> 324/500   risk = 1  
#> 336/500   risk = 1  
#> 348/500   risk = 1  
#> 360/500   risk = 1  
#> 372/500   risk = 1  
#> 384/500   risk = 1  
#> 396/500   risk = 1  
#> 408/500   risk = 1  
#> 420/500   risk = 1  
#> 432/500   risk = 1  
#> 444/500   risk = 1  
#> 456/500   risk = 1  
#> 468/500   risk = 1  
#> 480/500   risk = 1  
#> 492/500   risk = 1  
#> 
#> 
#> Train 500 iterations in 0 Seconds.
#> Final risk based on the train set: 1
#> 
plotBaselearner(cboost, "Sepal.Width_linear")
plotBaselearner(cboost, "Sepal.Width_spline_centered")