[{'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:"""\nDisadvantage with k-nearest nrighbor.\n1. The classifier has to store all the training records in the memory which is space inefficient\n2. The classifier has to iteratively compare test data to all of the training data.\n3. The nearest neighbor algorithm can not model the complexities in the image like edges, shapes. The classsifer\neven performs bad under different intensities of light. ... and many more\n"""\nclass KNN():\ndef __init__(self):\npass\ndef train(self,x, y):\nself.trainX = x\nself.trainY = y\ndef similarity(self, k, validX, distanceMeasure):\n\'\'\'\nHere we use different similarity measure to see that the cross validation data\nis closest to which image. The training set that has minimum distance with the\ncross validation dataset is the closest prediction\n\'\'\'\nif distanceMeasure == \'L1\':\ndistance = np.sum(np.abs(self.trainX - validX), axis = 1)\nkNearest_indices = distance.argsort()[:k]\nreturn kNearest_indices\nif distanceMeasure == \'L2\':\ndistance = np.sqrt(np.sum(np.square(self.trainX - validX), axis=1))\nkNearest_indices = distance.argsort()[:k]\nreturn kNearest_indices\ndef predict(self, k, validX, numValidation, distanceMeasure=\'L1\'):\npredY = np.zeros(shape=(numValidation, self.trainY.shape[1]), dtype=self.trainY.dtype)\npredY_data =  np.zeros(shape=(numValidation), dtype=int)\nfor vrow in range(numValidation):\nkNearest_indices = self.similarity(k, validX[vrow,:], distanceMeasure)\nkNearestNeighbor = self.trainY[kNearest_indices]\nsum_knn = sum(kNearestNeighbor)\nnearestLabel = (sum_knn == sum_knn.max(axis=0,keepdims=True)).astype(int)\nnearestLabelIndice = [i for i,j in zip(kNearest_indices, nearestLabel) if j==1]\npredY[vrow] = nearestLabel\npredY_data[vrow] = nearestLabelIndice[0]\nprint(\'Ran for %d cross validation rows and now returning : \'%numValidation)\nreturn predY, predY_data'}, {'role': 'assistant', 'content': 'modelling'}]