[{'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:import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import convolve\nimport csv\nimport os.path\nimport scipy.ndimage as nd\nimport pandas as pd\nimport random\nimport scipy\nimport time\nimport pandas as pd\nfrom sklearn import svm,cross_validation as cv\nfrom sklearn.ensemble import RandomForestClassifier\nIMAGE_WIDTH = 28\nUSE_PICKLE = False\ndef threshold(X):\nX[X < 0.15] = 0.0\nX[X >= 0.85] = 1.0\nreturn X\ndef rotate_dataset(X, Y):\nrot_X = np.zeros(X.shape)\nfor index in range(X.shape[0]):\nsign = np.random.choice([-1, 1])\nangle = np.random.randint(8, 16)*sign\nrot_X[index, :] = threshold(nd.rotate(np.reshape(X[index, :],\n((28, 28))), angle, reshape=False).ravel())\nXX = np.vstack((X,rot_X))\nYY = np.hstack((Y,Y))\nreturn XX, YY\ndef nudge_dataset(X, Y):\nnudge_size = 2\ndirection_matricies = [\n[[0, 1, 0],\n[0, 0, 0],\n[0, 0, 0]],\n[[0, 0, 0],\n[1, 0, 0],\n[0, 0, 0]],\n[[0, 0, 0],\n[0, 0, 1],\n[0, 0, 0]],\n[[0, 0, 0],\n[0, 0, 0],\n[0, 1, 0]]]\nscaled_direction_matricies = [[[comp*nudge_size for comp in vect] for vect in matrix] for matrix in direction_matricies]\nshift = lambda x, w: convolve(x.reshape((IMAGE_WIDTH, IMAGE_WIDTH)), mode=\'constant\',\nweights=w).ravel()\nX = np.concatenate([X] + [np.apply_along_axis(shift, 1, X, vector) for vector in scaled_direction_matricies])\nY = np.concatenate([Y for _ in range(5)], axis=0)\nreturn X, Y\nprint("done1")'}, {'role': 'assistant', 'content': 'helper_functions'}]