Jan-13-2018, 04:54 PM
how can i improve the code below, the problem lies with how y_pred is stored which i cant add to total. the idea is to calculate accuracy over 150 training examples and mean error rate.
if __name__ == "__main__": dataset = "car.data" attributes = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'] target = 'acceptability' samples = 150 total = 0.0 k=0 while (k<6): k = input("Enter the number of neighbours: ") k = int(k) for i in range(samples): y_pred = [] temp = KNN(X_train,y_train,X_test,y_pred,k) y_pred = np.asarray(y_pred) acc = accuracy_score(y_test, y_pred) total = total + temp acc = total/samples error_rate = 100 - avg_acc cm = confusion_matrix(y_test,y_pred) cr = classification_report(y_test,y_pred) print(acc)total = total + temp print("Accuracy:", +acc) print("Avg Accuracy:" +avg_acc) print(cm) print(cr)