[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:kfold = KFold(n_splits=10, random_state=10)<br>results = cross_val_score(model, trainingFeatures2, trainingLabels, cv=kfold)<br>#print("DecisionTreeClassifier:\\<br>\\<br>Cross_Val_Score: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))<br>print("Logistic Regression Classifier:\\<br>\\<br>Cross_Val_Score: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))<br>#origina<br>prediction = model.predict(X_test)<br><br>#X_test_scaler = scaler.fit_transform(X_test)<br>#prediction = model.predict(X_test_scaler)<br><br>cnf_matrix = confusion_matrix(y_test, prediction)<br>dict_characters = {0: \'Healthy\', 1: \'Diabetes\'}<br>plot_confusion_matrix(cnf_matrix, classes=dict_characters,title=\'Confusion matrix\')'}, {'role': 'assistant', 'content': 'evaluation'}]