Merge branch 'master' into fix_1738_svm_no_formula

Author: pat-s

.github/PULL_REQUEST_TEMPLATE.md

@@ -862,6 +862,7 @@ export(getHomogeneousEnsembleModels)
 export(getHyperPars)
 export(getLearnerId)
 export(getLearnerModel)
+export(getLearnerNote)
 export(getLearnerPackages)
 export(getLearnerParVals)
 export(getLearnerParamSet)

NAMESPACE

@@ -46,6 +46,17 @@ getLearnerPackages = function(learner) {
   return(learner$package)
 }
 
+#' @title Get the note for the learner.
+#'
+#' @description Get the note for the learner.
+#' @template arg_learner
+#' @return ([character]).
+#' @export
+#' @family learner
+getLearnerNote = function(learner) {
+  learner = checkLearner(learner)
+  return(learner$note)
+}
 
 #' @title Get the parameter set of the learner.
 #'

NEWS.md

@@ -40,6 +40,8 @@ trainLearner.classif.cforest = function(.learner, .task, .subset,
 
   f = getTaskFormula(.task)
   d = getTaskData(.task, .subset)
+
+  # default handling necessary because the default of controls is `cforest_unbiased()` which does not allow all parameters (e.g. replace)
   defaults = getDefaults(getParamSet(.learner))
   if (missing(teststat)) teststat = defaults$teststat
   if (missing(testtype)) testtype = defaults$testtype
@@ -50,6 +52,7 @@ trainLearner.classif.cforest = function(.learner, .task, .subset,
     fraction, trace, teststat, testtype, mincriterion,
     minsplit, minbucket, stump, nresample, maxsurrogate,
     maxdepth, savesplitstats)
+
   party::cforest(f, data = d, controls = ctrl, weights = .weights, ...)
 }
 

R/Learner_operators.R

@@ -39,6 +39,8 @@ trainLearner.regr.cforest = function(.learner, .task, .subset, .weights = NULL,
 
   f = getTaskFormula(.task)
   d = getTaskData(.task, .subset)
+
+  # default handling necessary because the default of controls is `cforest_unbiased()` which does not allow all parameters (e.g. replace)
   defaults = getDefaults(getParamSet(.learner))
   if (missing(teststat)) teststat = defaults$teststat
   if (missing(testtype)) testtype = defaults$testtype
@@ -49,6 +51,7 @@ trainLearner.regr.cforest = function(.learner, .task, .subset, .weights = NULL,
     trace, teststat, testtype, mincriterion,
     minsplit, minbucket, stump,
     nresample, maxsurrogate, maxdepth, savesplitstats)
+
   party::cforest(f, data = d, controls = ctrl, weights = .weights, ...)
 }
 

R/RLearner_classif_cforest.R

@@ -64,8 +64,7 @@ predictLearner.regr.crs = function(.learner, .model, .newdata, ...) {
     lwr = attr(pred, "lwr")
     attr(pred, "lwr") = NULL
     attr(pred, "upr") = NULL
-    # FIXME: make sure that this is correct, ask Daniel
-    se = (pred - lwr) * sqrt(.model$task.desc$size) / qnorm(0.95)
+    se = (pred - lwr) / qnorm(0.95)
     cbind(pred, se)
   } else {
     pred = predict(.model$learner.model, newdata = .newdata, ...)