diff --git a/ecnet/error_utils.py b/ecnet/error_utils.py
index f3fd771..f7a333a 100644
--- a/ecnet/error_utils.py
+++ b/ecnet/error_utils.py
@@ -19,9 +19,7 @@ def calc_rmse(y_hat, y):
 		try:
 			return(np.sqrt(((np.asarray(y_hat)-np.asarray(y))**2).mean()))
 		except:
-			print("Error in calculating RMSE. Check input data format.")
-			raise
-			sys.exit()
+			raise ValueError("Error in calculating RMSE. Check input data format.")
 			
 # Calculates the mean average error between two arguments of equal length 
 def calc_mean_abs_error(y_hat, y):
@@ -31,9 +29,7 @@ def calc_mean_abs_error(y_hat, y):
 		try:
 			return(abs(np.asarray(y_hat)-np.asarray(y)).mean())
 		except:
-			print("Error in calculating mean average error. Check input data format.")
-			raise
-			sys.exit()
+			raise ValueError("Error in calculating mean average error. Check input data format.")
 
 # Calculates the median absoltute error between two arguments of equal length			
 def calc_med_abs_error(y_hat, y):
@@ -43,10 +39,7 @@ def calc_med_abs_error(y_hat, y):
 		try:
 			return(np.median(np.absolute(np.asarray(y_hat)-np.asarray(y))))
 		except:
-			return("Error in calculating median absolute error. Check input data format.")
-			raise
-			sys.exit() 
-	
+			raise ValueError("Error in calculating median absolute error. Check input data format.")
 
 # Calculates the correlation of determination, or r-squared value, between two arguments of equal length
 def calc_r2(y_hat, y):
@@ -59,9 +52,7 @@ def calc_r2(y_hat, y):
 				y_form.append(y[i][0])
 			y_mean = sum(y_form)/len(y_form)
 		except:
-			print("Error in calculating r-squared. Check input data format.")
-			raise
-			sys.exit()
+			raise ValueError("Error in calculating r-squared. Check input data format.")
 	try:
 		s_res = np.sum((y_hat-y)**2)
 		s_tot = np.sum((y-y_mean)**2)
@@ -72,6 +63,4 @@ def calc_r2(y_hat, y):
 			s_tot = np.sum((np.asarray(y)-y_mean)**2)
 			return(1 - (s_res/s_tot))
 		except:
-			print("Error in calculating r-squared. Check input data format.")
-			raise
-			sys.exit()
+			raise ValueError("Error in calculating r-squared. Check input data format.")
diff --git a/ecnet/server.py b/ecnet/server.py
index cfcef8a..e823a64 100644
--- a/ecnet/server.py
+++ b/ecnet/server.py
@@ -180,6 +180,7 @@ def test_neural_network(values):
 				amountOfEmployers = amt_employers)
 
 		# Run the artificial bee colony
+		abc.printInfo(self.vars['project_print_feedback'])
 		new_hyperparameters = abc.runABC()
 
 		# Set Server hyperparameters to ABC-calculated hyperparameters
@@ -394,7 +395,7 @@ def calc_error(self, *args, dset = None):
 		return error_dict
 
 	'''
-	Outputs the *results* to a specified *filename*
+	Outputs the *results* obtained from "use_model()" to a specified *filename*
 	'''
 	def output_results(self, results, filename = 'my_results.csv'):
 
@@ -567,4 +568,4 @@ def __error_fn(self, arg, y_hat, y):
 		elif arg == 'med_abs_error':
 			return ecnet.error_utils.calc_med_abs_error(y_hat, y)
 		else:
-			raise Exception('ERROR: Unknown/unsupported error function')
\ No newline at end of file
+			raise Exception('ERROR: Unknown/unsupported error function')
diff --git a/examples/limit_input_parameters.py b/examples/limit_input_parameters.py
index 912828e..ab12b84 100644
--- a/examples/limit_input_parameters.py
+++ b/examples/limit_input_parameters.py
@@ -7,9 +7,9 @@
 # Import data (change 'my_data.csv' to your database name)
 sv.import_data('my_data.csv')
 
-# Limit the input dimensionality to 15, save to 'my_data_limite.csv'
+# Limit the input dimensionality to 15, save to 'my_data_limited.csv'
 sv.limit_parameters(15, 'my_data_limited.csv')
 
 
 # Use this line instead for limiting the input dimensionality using a genetic algorithm
-sv.limit_parameters(15, 'my_data_limited.csv', use_genetic = True)
\ No newline at end of file
+sv.limit_parameters(15, 'my_data_limited_genetic.csv', use_genetic = True)