[{'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:N_PROCS = 8\ndef unwrapLS(arg, **kwarg):\nreturn CLFLS.oneFit(*arg, **kwarg)\nclass CLFLS(BaseEstimator, ClassifierMixin):\ndef __init__(self, X, y, param, final):\nself.X = X\nself.y = y\nself.param = param\nself.final = final\nself.clf = []\ndef oneFit(self, i):\nif len(set(self.y[:, i])) == 2:\nreturn LinearSVC(C = self.param).fit(self.X, self.y[:, i])\ndef fit(self):\nclass_pool = Pool(processes = N_PROCS)\nself.clf = class_pool.map(unwrapLS, zip([self] * 200, array(range(200))))\nclass_pool.close()\nclass_pool.join()\ndef predict(self, X):\npredicted = []\nfor i in range(200):\nif len(set(self.y[:, i])) == 2:\npred = self.clf[i].decision_function(X)\nelse:\npred = [-10] * shape(X)[0]\npredicted.append(pred)\ndel self.clf\ngc.collect()\nreturn array(predicted).T'}, {'role': 'assistant', 'content': "['modelling', 'prediction']"}]