else:
X_train = X_train.append(folds[x])
y_train = X_train['class'].values
X_train = X_train.drop(['class'], axis=1)
nb = NaiveBayes() #initialize Naive Bayes Classifier
nb.fit(X_train, y_train) #train model with train data
y_pred = nb.predict(X_test) #test model with test set
#find error with respect to zero-one loss function
error = nb.zero_one_loss_function(y_test, y_pred)
printstr = "\nAccuracy of 0-1 loss for fold {0} ::: {1}".format(
y, (1 - error))
print_both(file, printstr)
accuracy_list.append((1 - error))
#get mean square error
acc, precision, recall = nb.confusion_matrix(y_test, y_pred)
printstr = "\nCF for fold {0} ::: acc:: {1} :: precision:: {2} :: recall :: {3}".format(
y, acc, precision, recall)
print_both(file, printstr)
CF_accuracy_list.append(acc)
CF_precision_list.append(precision)
CF_recall_list.append(recall)
test_set += 1 #increment test_set to set for the next fold
result_set.update({dataset: accuracy_list})
CF_acc_result_set.update({dataset: CF_accuracy_list})
CF_prec_result_set.update({dataset: CF_precision_list})
CF_recall_result_set.update({dataset: CF_recall_list})
#express accuracy in terms of graph
result_inorder = sorted(result_set.keys())
#increment x by 2 to get the actual and noise dataset graph for each dataset
