diff --git a/script5_4.py b/script5_4.py
index ea63d99..73c590b 100644
--- a/script5_4.py
+++ b/script5_4.py
@@ -4,7 +4,7 @@ import torch.nn as nn
 from torch.utils.data import DataLoader, Dataset
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder
-from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
+from sklearn.metrics import mean_squared_error
 import pickle
 
 # Define the neural network model
@@ -75,14 +75,6 @@ for inputs, targets_batch in test_dataloader:
 predictions = torch.FloatTensor(predictions).squeeze()
 targets = torch.FloatTensor(targets).squeeze()
 
-accuracy = accuracy_score(targets, torch.round(predictions))
-precision = precision_score(targets, torch.round(predictions), average='micro')
-recall = recall_score(targets, torch.round(predictions), average='micro')
-f1 = f1_score(targets, torch.round(predictions), average='micro')
 rmse = mean_squared_error(targets, predictions, squared=False)
 
-print("Accuracy:", accuracy)
-print("Micro-average Precision:", precision)
-print("Micro-average Recall:", recall)
-print("F1 Score:", f1)
 print("RMSE:", rmse)