[{'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 math\ndef maxinfo(data):\nentropy = np.zeros(data.shape[1])\nfor i in range(data.shape[1]):\nentropy[i] = _entropy(data, i)\nreturn entropy\ndef _entropy(data, target_attr):\nval_freq = data.iloc[:,target_attr].value_counts()\ndata_entropy = 0.0\nfor freq in val_freq:\ndata_entropy += (-freq/len(data)) * math.log(freq/len(data), 2)\nreturn data_entropy\ndef gain(data, attr, target_attr):\nsubset_entropy = 0.0\nval_freq = data.iloc[:,target_attr].value_counts()\nfor val in val_freq.keys():\nval_prob = freq/len(data)\ndata_subset = [record for record in data if record[attr] == val]\nsubset_entropy += val_prob * entropy(data_subset, target_attr)\nreturn (entropy(data, target_attr) - subset_entropy)'}, {'role': 'assistant', 'content': "['helper_functions', 'modelling']"}]