[{'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:import numpy as np\nimport pandas as pd\nimport random\nimport matplotlib.pyplot as plt\n\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.ensemble import BaggingClassifier\nfrom sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\nfrom sklearn.neighbors import NearestCentroid, RadiusNeighborsClassifier, KNeighborsClassifier\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.gaussian_process import GaussianProcessClassifier\nfrom sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\nfrom scipy import stats\nfrom sklearn.svm import SVC, LinearSVC, NuSVC\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import normalize\nfrom sklearn.utils import shuffle\nfrom sklearn import decomposition, cross_decomposition\n\n# Input data files are available in the "../input/" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir("../input"))\n\n# number of attributes for pca\ndecompose_to = 5'}, {'role': 'assistant', 'content': "['helper_functions']"}]