[{'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:train_labels = np.tile(labels, subjects-1)\ntest_labels = labels\naccuracy = np.zeros((subjects))\ncm = [None] * subjects\nfor subject in range(subjects):\ntrain_subjects = list(range(subjects))\ntrain_subjects.remove(subject)\nTRs = image_data_shared[0].shape[1]\ntrain_data = np.zeros((image_data_shared[0].shape[0], len(train_labels)))\nfor train_subject in range(len(train_subjects)):\nstart_index = train_subject*TRs\nend_index = start_index+TRs\ntrain_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]\nclassifier = NuSVC(nu=0.5, kernel='linear')\nclassifier = classifier.fit(train_data.T, train_labels)\npredicted_labels = classifier.predict(image_data_shared[subject].T)\naccuracy[subject] = sum(predicted_labels == test_labels)/float(len(predicted_labels))\ncm[subject] = confusion_matrix(test_labels, predicted_labels)\ncm[subject] = cm[subject].astype('float') / cm[subject].sum(axis=1)[:, np.newaxis]"}, {'role': 'assistant', 'content': 'modelling'}]