How to use breakDown package for SVM models

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Przemyslaw Biecek

2024-03-11

This example demonstrates how to use the breakDown package for models created with the kernlab package.

library("breakDown")
library(kernlab)
#> 
#> Attaching package: 'kernlab'
#> The following object is masked from 'package:ggplot2':
#> 
#>     alpha

wine_svm_model <- ksvm(quality~., data = wine)
wine_svm_model
#> Support Vector Machine object of class "ksvm" 
#> 
#> SV type: eps-svr  (regression) 
#>  parameter : epsilon = 0.1  cost C = 1 
#> 
#> Gaussian Radial Basis kernel function. 
#>  Hyperparameter : sigma =  0.083888616644614 
#> 
#> Number of Support Vectors : 4187 
#> 
#> Objective Function Value : -2260.233 
#> Training error : 0.519335

# or with the e1071:::svm

library(e1071)
wine_svm_model <- svm(quality~., data = wine)
wine_svm_model
#> 
#> Call:
#> svm(formula = quality ~ ., data = wine)
#> 
#> 
#> Parameters:
#>    SVM-Type:  eps-regression 
#>  SVM-Kernel:  radial 
#>        cost:  1 
#>       gamma:  0.09090909 
#>     epsilon:  0.1 
#> 
#> 
#> Number of Support Vectors:  4176

Now we are ready to call the broken() function. Since kernlab is useing S4 methods we need to pass here the hook to kernlab:::predict method.

library("breakDown")
nobs <- wine[5, , drop = FALSE]
base_prediction <- predict(wine_svm_model, nobs)
set.seed(1313)

explain_5_up <- broken(wine_svm_model, new_observation = nobs, 
                    data = wine, predict.function = predict,
                    baseline = "intercept", direction = "up")
explain_5_up
#>                              contribution
#> (Intercept)                         0.000
#> + density = 0.9956                 -0.290
#> + chlorides = 0.058                -0.061
#> + alcohol = 9.9                    -0.079
#> + pH = 3.19                        -0.031
#> + total.sulfur.dioxide = 186       -0.023
#> + sulphates = 0.4                  -0.027
#> + free.sulfur.dioxide = 47         -0.005
#> + quality = 6                       0.000
#> + citric.acid = 0.32                0.019
#> + residual.sugar = 8.5              0.098
#> + volatile.acidity = 0.23           0.102
#> + fixed.acidity = 7.2               0.146
#> final_prognosis                    -0.152
#> baseline:  5.842347

explain_5_down <- broken(wine_svm_model, new_observation = nobs, 
                    data = wine, predict.function = predict,
                    baseline = "intercept", direction = "down")
explain_5_down
#>                              contribution
#> (Intercept)                         0.000
#> - density = 0.9956                 -0.290
#> - residual.sugar = 8.5              0.195
#> - alcohol = 9.9                    -0.080
#> - volatile.acidity = 0.23           0.103
#> - total.sulfur.dioxide = 186       -0.071
#> - sulphates = 0.4                  -0.066
#> - fixed.acidity = 7.2               0.072
#> - pH = 3.19                        -0.025
#> - citric.acid = 0.32                0.049
#> - free.sulfur.dioxide = 47         -0.038
#> - chlorides = 0.058                -0.001
#> - quality = 6                       0.000
#> final_prognosis                    -0.152
#> baseline:  5.842347

library(ggplot2)
plot(explain_5_up) + ggtitle(paste0("Prediction for SVM model ", round(base_prediction, 3)))

plot(explain_5_down) +  ggtitle(paste0("Prediction for SVM model ", round(base_prediction, 3)))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.