diff --git a/NAMESPACE b/NAMESPACE
index 7efc6821..4f91a67e 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,14 +1,4 @@
 import(jaspBase)
-S3method(.decodeJaspMLobject,kknn)
-S3method(.decodeJaspMLobject,lda)
-S3method(.decodeJaspMLobject,lm)
-S3method(.decodeJaspMLobject,gbm)
-S3method(.decodeJaspMLobject,randomForest)
-S3method(.decodeJaspMLobject,cv.glmnet)
-S3method(.decodeJaspMLobject,nn)
-S3method(.decodeJaspMLobject,rpart)
-S3method(.decodeJaspMLobject,svm)
-S3method(.decodeJaspMLobject,naiveBayes)
 S3method(.mlPredictionGetModelType,kknn)
 S3method(.mlPredictionGetModelType,lda)
 S3method(.mlPredictionGetModelType,lm)
diff --git a/R/mlPrediction.R b/R/mlPrediction.R
index dfae59ac..5128061f 100644
--- a/R/mlPrediction.R
+++ b/R/mlPrediction.R
@@ -249,7 +249,7 @@ is.jaspMachineLearning <- function(x) {
       dataset <- .scaleNumericData(dataset)
     }
     # Select only the predictors in the model to prevent accidental double column names
-    dataset <- dataset[, which(decodeColNames(colnames(dataset)) %in% model[["jaspVars"]][["decoded"]]$predictors)]
+    dataset <- dataset[, which(decodeColNames(colnames(dataset)) %in% model[["jaspVars"]][["decoded"]]$predictors), drop = FALSE]
     # Ensure the column names in the dataset match those in the training data
     colnames(dataset) <- .matchDecodedNames(colnames(dataset), model)
     # Retrieve the training set
diff --git a/inst/qml/mlRegressionKnn.qml b/inst/qml/mlRegressionKnn.qml
index e9cb9b88..e95b7f3b 100644
--- a/inst/qml/mlRegressionKnn.qml
+++ b/inst/qml/mlRegressionKnn.qml
@@ -50,7 +50,7 @@ Form
 		KNN.WeightFunction { }
 	}
 
-	UI.ExportResults { enabled: vars.predictorCount > 1 && vars.targetCount > 0 }
+	UI.ExportResults { enabled: vars.predictorCount > 0 && vars.targetCount > 0 }
 
 	UI.DataSplit { trainingValidationSplit: !optim.isManual }
 
diff --git a/inst/qml/mlRegressionLinear.qml b/inst/qml/mlRegressionLinear.qml
index ef7e35b8..3dd82086 100644
--- a/inst/qml/mlRegressionLinear.qml
+++ b/inst/qml/mlRegressionLinear.qml
@@ -49,7 +49,7 @@ Form
 		FIG.PredictivePerformance { }
 	}
 
-	UI.ExportResults { enabled:	vars.predictorCount > 1 > 0 && vars.targetCount > 0 }
+	UI.ExportResults { enabled:	vars.predictorCount > 0 && vars.targetCount > 0 }
 	UI.DataSplit { trainingValidationSplit: false }
 
 	Section