title: SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
body: <p>I met problems when trying to output prediction probabilities of SVM using mlr3.</p>
  
  <pre><code>library(mlr3)
  task = mlr_tasks$get("iris")
  svm_learner = mlr_learners$get("classif.svm")
  train_set = sample(task$nrow, 0.8 * task$nrow)
  test_set = setdiff(seq_len(task$nrow), train_set)
  
  svm_learner$train(task, row_ids = task$row_ids[train_set])
  svm_learner$predict_type&lt;-"prob"
  prediction&lt;-svm_learner$predict(task,row_ids = task$row_ids[test_set])
  prediction
  Warning message:
  In predict.svm(self$model, newdata = newdata, probability = (self$predict_type ==  :
    SVM has not been trained using `probability = TRUE`, probabilities not available for predictions.
  
  
  
  Session info
  &gt; sessionInfo(package = NULL)
  R version 3.6.2 (2019-12-12)
  Platform: x86_64-w64-mingw32/x64 (64-bit)
  Running under: Windows 10 x64 (build 17763)
  
  Matrix products: default
  
  Random number generation:
   RNG:     Mersenne-Twister 
   Normal:  Inversion 
   Sample:  Rounding 
  
  locale:
  [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
  [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
  [5] LC_TIME=English_United States.1252    
  
  attached base packages:
  [1] stats     graphics  grDevices utils     datasets  methods   base     
  
  other attached packages:
   [1] precrec_0.10.1     forcats_0.4.0      stringr_1.4.0      purrr_0.3.3        readr_1.3.1       
   [6] tidyr_1.0.0        tibble_2.1.3       tidyverse_1.2.1    dplyr_0.8.3        mlr3learners_0.1.5
  [11] GGally_1.4.0       ggplot2_3.2.1      mlr3_0.1.6         mlr3viz_0.1.0      e1071_1.7-3       
  [16] biomaRt_2.38.0    
  
  loaded via a namespace (and not attached):
   [1] Biobase_2.42.0       httr_1.4.1           bit64_0.9-7          jsonlite_1.6        
   [5] modelr_0.1.4         assertthat_0.2.1     lgr_0.3.3            stats4_3.6.2        
   [9] blob_1.2.0           cellranger_1.1.0     mlr3misc_0.1.6       progress_1.2.2      
  [13] pillar_1.4.3         RSQLite_2.1.2        backports_1.1.5      lattice_0.20-38     
  [17] glue_1.3.1           uuid_0.1-2           digest_0.6.23        RColorBrewer_1.1-2  
  [21] checkmate_1.9.4      rvest_0.3.3          colorspace_1.4-1     plyr_1.8.5          
  [25] XML_3.98-1.20        pkgconfig_2.0.3      mlr3measures_0.1.1   broom_0.5.2         
  [29] haven_2.1.0          scales_1.0.0         generics_0.0.2       IRanges_2.16.0      
  [33] withr_2.1.2          BiocGenerics_0.28.0  lazyeval_0.2.2       cli_2.0.0           
  [37] magrittr_1.5         crayon_1.3.4         readxl_1.3.1         paradox_0.1.0       
  [41] memoise_1.1.0        fansi_0.4.0          nlme_3.1-142         xml2_1.2.0          
  [45] class_7.3-15         tools_3.6.2          data.table_1.12.8    prettyunits_1.0.2   
  [49] hms_0.5.2            lifecycle_0.1.0      S4Vectors_0.20.1     munsell_0.5.0       
  [53] AnnotationDbi_1.44.0 compiler_3.6.2       rlang_0.4.1          grid_3.6.2          
  [57] RCurl_1.95-4.12      rstudioapi_0.10      bitops_1.0-6         labeling_0.3        
  [61] gtable_0.3.0         DBI_1.0.0            reshape_0.8.8        reshape2_1.4.3      
  [65] R6_2.4.1             lubridate_1.7.4      bit_1.1-14           zeallot_0.1.0       
  [69] stringi_1.4.3        parallel_3.6.2       Rcpp_1.0.2           vctrs_0.2.1         
  [73] tidyselect_0.2.5
  </code></pre>
  
  <p>I understand that SVM will not output a probability, but SVM can fit the prediction data to the segregation hyperplane function and get a signed distance measure from the hyperplane. I want to retrieve the signed distance, and then use them to calculate AUC. 
  But with <code>predict_type&lt;-"response"</code>, I can only get the predicted class, not the signed distance. With <code>predict_type&lt;-"probability"</code>, I got the error above.</p>