diff --git a/R/commonMachineLearningClassification.R b/R/commonMachineLearningClassification.R
index 3e7a01e2..9afb3656 100644
--- a/R/commonMachineLearningClassification.R
+++ b/R/commonMachineLearningClassification.R
@@ -167,16 +167,9 @@
   if (!ready) {
     table$addFootnote(gettextf("Please provide a target variable and at least %i feature variable(s).", if (type == "knn" || type == "neuralnet" || type == "rpart" || type == "svm" || type == "logistic") 1L else 2L))
   }
-  if (options[["saveModel"]]) {
-    validNames <- (length(grep(" ", decodeColNames(colnames(dataset)))) == 0) && (length(grep("_", decodeColNames(colnames(dataset)))) == 0)
-    if (options[["savePath"]] != "" && validNames) {
-      table$addFootnote(gettextf("The trained model is saved as <i>%1$s</i>.", basename(options[["savePath"]])))
-    } else if (options[["savePath"]] != "" && !validNames) {
-      table$addFootnote(gettext("The trained model is <b>not</b> saved because the some of the variable names in the model contain spaces (i.e., ' ') or underscores (i.e., '_'). Please remove all such characters from the variable names and try saving the model again."))
-    } else {
-      table$addFootnote(gettext("The trained model is not saved until a file name is specified under 'Save as'."))
-    }
-  }
+
+  .mlAddSaveModelInfo(table, options)
+
   jaspResults[["classificationTable"]] <- table
   if (!ready) {
     return()
@@ -330,26 +323,11 @@
     )
     table$addRows(row)
   }
-  # Save the applied model if requested
-  if (options[["saveModel"]] && options[["savePath"]] != "") {
-    validNames <- (length(grep(" ", decodeColNames(colnames(dataset)))) == 0) && (length(grep("_", decodeColNames(colnames(dataset)))) == 0)
-    if (!validNames) {
-      return()
-    }
-    model <- classificationResult[["model"]]
-    model[["jaspVars"]] <- list()
-    model[["jaspVars"]]$decoded <- list(target = decodeColNames(options[["target"]]), predictors = decodeColNames(options[["predictors"]]))
-    model[["jaspVars"]]$encoded = list(target = options[["target"]], predictors = options[["predictors"]])
-    model[["jaspScaling"]] <- attr(dataset, "jaspScaling")
-    model[["jaspVersion"]] <- .baseCitation
-    model[["explainer"]] <- classificationResult[["explainer"]]
-    class(model) <- c(class(classificationResult[["model"]]), "jaspClassification", "jaspMachineLearning")
-    path <- options[["savePath"]]
-    if (!endsWith(path, ".jaspML")) {
-      path <- paste0(path, ".jaspML")
-    }
-    saveRDS(model, file = path)
-  }
+
+  # Save the model if requested
+  saveResult <- .mlSaveModelToDisk(options, classificationResult, dataset, class = "jaspClassification")
+  .mlPossiblyShowSaveResult(table, saveResult, options)
+
 }
 
 .mlClassificationTableConfusion <- function(dataset, options, jaspResults, ready, position) {
diff --git a/R/commonMachineLearningRegression.R b/R/commonMachineLearningRegression.R
index 61fd1596..69746a8c 100644
--- a/R/commonMachineLearningRegression.R
+++ b/R/commonMachineLearningRegression.R
@@ -166,7 +166,7 @@
   if (length(factorsWithNewLevels) > 0) {
     setType <- switch(type, "test" = gettext("test set"), "validation" = gettext("validation set"), "prediction" = gettext("new dataset"))
     additionalMessage <- switch(type,
-      "test" = gettext(" or use a different test set (e.g., automatically by setting a different seed or manually by specifying the test set indicator)"), 
+      "test" = gettext(" or use a different test set (e.g., automatically by setting a different seed or manually by specifying the test set indicator)"),
       "validation" = gettext(" or use a different validation set by setting a different seed"),
       "prediction" = "")
     factorMessage <- paste(sapply(factorsWithNewLevels, function(i) {
@@ -337,16 +337,9 @@
   if (!ready) {
     table$addFootnote(gettextf("Please provide a target variable and at least %d feature variable(s).", if (type == "knn" || type == "neuralnet" || type == "rpart" || type == "svm" || type == "lm") 1L else 2L))
   }
-  if (options[["saveModel"]]) {
-    validNames <- (length(grep(" ", decodeColNames(colnames(dataset)))) == 0) && (length(grep("_", decodeColNames(colnames(dataset)))) == 0)
-    if (options[["savePath"]] != "" && validNames) {
-      table$addFootnote(gettextf("The trained model is saved as <i>%1$s</i>.", basename(options[["savePath"]])))
-    } else if (options[["savePath"]] != "" && !validNames) {
-      table$addFootnote(gettext("The trained model is <b>not</b> saved because the some of the variable names in the model contain spaces (i.e., ' ') or underscores (i.e., '_'). Please remove all such characters from the variable names and try saving the model again."))
-    } else {
-      table$addFootnote(gettext("The trained model is not saved until a file name is specified under 'Save as'."))
-    }
-  }
+
+  .mlAddSaveModelInfo(table, options)
+
   jaspResults[["regressionTable"]] <- table
   if (!ready) {
     return()
@@ -492,25 +485,63 @@
     )
     table$addRows(row)
   }
+
   # Save the model if requested
-  if (options[["saveModel"]] && options[["savePath"]] != "") {
-    validNames <- (length(grep(" ", decodeColNames(colnames(dataset)))) == 0) && (length(grep("_", decodeColNames(colnames(dataset)))) == 0)
-    if (!validNames) {
-      return()
-    }
-    model <- regressionResult[["model"]]
+  saveResult <- .mlSaveModelToDisk(options, regressionResult, dataset, class = "jaspRegression")
+  .mlPossiblyShowSaveResult(table, saveResult, options)
+
+}
+
+.mlAddSaveModelInfo <- function(table, options) {
+  if (options[["saveModel"]] && options[["savePath"]] == "") {
+    table$addFootnote(gettext("The trained model is not saved until a file name is specified under 'Save as'."))
+  }
+}
+
+.mlSaveModelToDisk <- function(options, mlResult, dataset, class = c("jaspRegression", "jaspClassification")) {
+
+  if (!options[["saveModel"]] || options[["savePath"]] == "")
+    return()
+
+  class <- match.arg(class)
+
+  error <- try({
+    model <- mlResult[["model"]]
     model[["jaspVars"]] <- list()
     model[["jaspVars"]]$decoded <- list(target = decodeColNames(options[["target"]]), predictors = decodeColNames(options[["predictors"]]))
     model[["jaspVars"]]$encoded = list(target = options[["target"]], predictors = options[["predictors"]])
     model[["jaspScaling"]] <- attr(dataset, "jaspScaling")
     model[["jaspVersion"]] <- .baseCitation
-    model[["explainer"]] <- regressionResult[["explainer"]]
-    class(model) <- c(class(regressionResult[["model"]]), "jaspRegression", "jaspMachineLearning")
+    model[["explainer"]] <- mlResult[["explainer"]]
+    class(model) <- c(class(mlResult[["model"]]), class, "jaspMachineLearning")
     path <- options[["savePath"]]
     if (!endsWith(path, ".jaspML")) {
       path <- paste0(path, ".jaspML")
     }
     saveRDS(model, file = path)
+
+    "success"
+  })
+  return(list(exists = file.exists(path), error = error))
+}
+
+.mlPossiblyShowSaveResult <- function(table, saveResult, options) {
+
+  if (is.null(saveResult))
+    return()
+
+  if (identical(saveResult[["error"]], "success") && isTRUE(saveResult[["exists"]])) {
+    table$addFootnote(gettextf("The model is saved as <i>%1$s</i>.", basename(options[["savePath"]])))
+  } else if (isTryError(saveResult[["error"]])) {
+    msg <- .extractErrorMessage(saveResult[["error"]])
+    if (grepl(x = msg, pattern = "cannot open the connection", fixed = TRUE) && !dir.exists(dirname(options[["savePath"]]))) {
+      # likely occurs most often when using a downloaded jasp file that contains a path that is valid another computer
+      table$addFootnote(gettextf("The model could not be saved because the parent directory '%s' does not exist.", dirname(options[["savePath"]])))
+    } else
+      table$addFootnote(gettextf("The model could not be saved because the following error occured: %s", msg))
+
+  } else if (!isTRUE(saveResult[["exists"]])) {
+    table$addFootnote(gettext("The model could not be saved because an unexpected error occured."))
   }
 }