This function visualizes the contribution of a specific feature to the overall prediction score. If multiple base learner of the same features are included, they are all added to the graphic as well as the aggregated contribution. The difference to plotBaselearner is that all base learners are visualized while plotBaselearner only visualizes one specific base learner. The function also automatically decides whether the given feature is numeric or categorical and chooses an appropriate technique (lines for numeric and horizontal lines for categorical).

plotPEUni(cboost, feat, npoints = 100L, individual = TRUE)

Arguments

cboost

[Compboost class]
A trained Compboost object.

feat

[character(1L)]
Name of the feature.

npoints

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

individual

[logical(1L)]
Flag whether individual base learners should be added to the graphic or not.

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
#> 
plotPEUni(cboost, "Sepal.Width")