[{'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:print(\'Vectorization...\')\n"""\nx\n(len(questions), MAxLEN, len(chars)) question/query tensor\nthere are len(questions) questions in total\neach question is represented as a (MAxLEN, len(chars)) matrix\nfor each query string the length is MAxLEN, thus is rows\nfor each character in the query string, a one-hot encoded vector is present with len(chars) size (vocab size)\nbool type is enough for one-hot encoding representations\n"""\nx = np.zeros((len(questions), MAxLEN, len(chars)), dtype = np.bool)\n"""\ny\n(len(questions), DIGITS + 1, len(chars)) answer tensor\nanswer number is len(questions), the same as x\nfor each answer, the length is DIGITS + 1 (\'999+999\' has 4 digits at most)\neach digit is also represented by he one-hot representations for the same char set\n"""\ny = np.zeros((len(questions), DIGITS + 1, len(chars)), dtype = np.bool)\nfor i, sentence in enumerate(questions):\nx[i] = ctable.encode(sentence, MAxLEN)\nfor i, sentence in enumerate(expected):\ny[i] = ctable.encode(sentence, DIGITS + 1)'}, {'role': 'assistant', 'content': "['data_exploration']"}]