[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:#model = DecisionTreeClassifier(max_depth=2,min_samples_leaf=2)<br>model = GradientBoostingClassifier(n_estimators=110, max_depth=3, min_samples_split=310, min_samples_leaf=5, max_features=7, subsample=0.85, learning_rate=0.1)<br>#model = XGBClassifier( learning_rate =0.1, n_estimators=10, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8, colsample_bytree=0.8, objective= \'binary:logistic\', nthread=4, scale_pos_weight=1, seed=27)<br>kfold = KFold(n_splits=10, random_state=7)<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><br>model.fit(trainingFeatures2, trainingLabels)'}, {'role': 'assistant', 'content': "['modelling']"}]