diff --git a/dataloader/dataloader.hpp b/dataloader/dataloader.hpp
index fe7b0b2c..9b0e9c9d 100644
--- a/dataloader/dataloader.hpp
+++ b/dataloader/dataloader.hpp
@@ -63,14 +63,14 @@ class DataLoader
    * 
    * @param datasetPath Path or name of dataset.
    * @param shuffle whether or not to shuffle the data.
-   * @param testRatio Ratio of dataset to be used for validation set.
+   * @param validRatio Ratio of dataset to be used for validation set.
    * @param useScaler Use feature scaler for pre-processing the dataset.
    * @param augmentation Adds augmentation to training data only.
    * @param augmentationProbability Probability of applying augmentation on dataset.
    */
   DataLoader(const std::string& dataset,
              const bool shuffle,
-             const double testRatio = 0.25,
+             const double validRatio = 0.25,
              const bool useScaler = true,
              const std::vector<std::string> augmentation =
                  std::vector<std::string>(),
@@ -85,7 +85,7 @@ class DataLoader
    *                      Note: This option augmentation to NULL, set ratio to 1 and
    *                      scaler will be used to only transform the test data.
    * @param shuffle Boolean to determine whether or not to shuffle the data.
-   * @param testRatio Ratio of dataset to be used for validation set.
+   * @param validRatio Ratio of dataset to be used for validation set.
    * @param useScaler Fits the scaler on training data and transforms dataset.
    * @param dropHeader Drops the first row from CSV.
    * @param startInputFeatures First Index which will be fed into the model as input.
@@ -106,7 +106,7 @@ class DataLoader
   void LoadCSV(const std::string& datasetPath,
                const bool loadTrainData = true,
                const bool shuffle = true,
-               const double testRatio = 0.25,
+               const double validRatio = 0.25,
                const bool useScaler = false,
                const bool dropHeader = false,
                const int startInputFeatures = -1,
diff --git a/dataloader/dataloader_impl.hpp b/dataloader/dataloader_impl.hpp
index b514fee9..d58f4939 100644
--- a/dataloader/dataloader_impl.hpp
+++ b/dataloader/dataloader_impl.hpp
@@ -36,7 +36,7 @@ template<
     DatasetX, DatasetY, ScalerType
 >::DataLoader(const std::string& dataset,
               const bool shuffle,
-              const double testRatio,
+              const double validRatio,
               const bool useScaler,
               const std::vector<std::string> augmentation,
               const double augmentationProbability)
@@ -49,14 +49,14 @@ template<
 
     if (datasetMap[dataset].loadCSV)
     {
-      LoadCSV(datasetMap[dataset].trainPath, true, shuffle, testRatio,
+      LoadCSV(datasetMap[dataset].trainPath, true, shuffle, validRatio,
               useScaler, datasetMap[dataset].dropHeader,
               datasetMap[dataset].startTrainingInputFeatures,
               datasetMap[dataset].endTrainingInputFeatures,
               datasetMap[dataset].endTrainingPredictionFeatures,
               datasetMap[dataset].endTrainingPredictionFeatures);
 
-      LoadCSV(datasetMap[dataset].testPath, false, false, testRatio, useScaler,
+      LoadCSV(datasetMap[dataset].testPath, false, false, validRatio, useScaler,
               datasetMap[dataset].dropHeader,
               datasetMap[dataset].startTestingInputFeatures,
               datasetMap[dataset].endTestingInputFeatures);
@@ -85,7 +85,7 @@ template<
 >::LoadCSV(const std::string& datasetPath,
            const bool loadTrainData,
            const bool shuffle,
-           const double testRatio,
+           const double validRatio,
            const bool useScaler,
            const bool dropHeader,
            const int startInputFeatures,
@@ -104,7 +104,7 @@ template<
   if (loadTrainData)
   {
     arma::mat trainDataset, validDataset;
-    data::Split(dataset, trainDataset, validDataset, testRatio, shuffle);
+    data::Split(dataset, trainDataset, validDataset, validRatio, shuffle);
 
     trainFeatures = trainDataset.rows(WrapIndex(startInputFeatures,
         trainDataset.n_rows), WrapIndex(endInputFeatures,