[{'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 time\nimport pandas as pd\nfrom sklearn.cross_validation import train_test_split\n\nfrom sklearn import ensemble\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\n\nloc_test = "../input/test.csv"\nloc_train = "../input/train.csv"\nloc_submission = "forest-cover-type-prediction.AspiringGuru.csv"\n\ndf_test = pd.read_csv(loc_test)\ndf_train = pd.read_csv(loc_train)\n\nprint ("type(df_test)=", type(df_test), "df_test.shape=", df_test.shape)\nprint ("type(df_train)=", type(df_train), "df_train.shape=", df_train.shape)\n\n#build list of all columns except the ones we don\'t want.\n# (\'Cover_Type\' is the predicted value), \'Id\' is a unique row identifier\nfeature_cols = [col for col in df_train.columns if col not in [\'Cover_Type\', \'Id\']]\n\n#create dataframe of the columns desired from the input data for test and train\nX_train = df_train[feature_cols]\nX_test = df_test[feature_cols]\n#create dataframe of the predicted value to use for building classifier\ntrain_y = df_train[\'Cover_Type\']\n#\ntest_ids = df_test[\'Id\']\n#test_y = df_test[\'Cover_Type\']\n\ndel df_train\ndel df_test\n\nprint ("creating classifier")\nstart_time = time.time()\nclf = ensemble.RandomForestClassifier(n_estimators=200, n_jobs=-1, random_state=0)\nprint("--- time to build ensemble.RandomForestClassifier %s seconds ---" % (time.time() - start_time))\n#http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html\n#n_jobs=-1 : the number of jobs is set to the number of cores.(runs faster)\n#n_estimators = The number of trees in the forest.\n\nprint ("fitting from train data")\nstart_time = time.time()\nclf.fit(X_train, train_y)\nprint("--- time to clf.fit %s seconds ---" % (time.time() - start_time))\n\nprint ("predicting from train data")\nstart_time = time.time()\ntrain_y_predicted = clf.predict(X_train)\nprint("--- time to clf.predict %s seconds ---" % (time.time() - start_time))\nprint ("type(train_y_predicted)=", type(train_y_predicted), "len(train_y_predicted)", len(train_y_predicted), "train_y_predicted.shape", train_y_predicted.shape)\nprint ("type(train_y)=", type(train_y), "train_y.shape=", train_y.shape)\nprint ("train_y = ", list(train_y[0:20, ]) )\nprint ("train_y_predicted = ", list(train_y_predicted[0:20, ]) )\n\nprint ("predicting from test data")\nstart_time = time.time()\npredicted = clf.predict(X_test)\nprint ("--- time to clf.predict %s seconds ---" % (time.time() - start_time))\nprint ("type(predicted)=", type(predicted), "len(predicted)", len(predicted) )\nprint  ("type(test_ids)=", type(test_ids), "len(test_ids)=", len(test_ids) )\n\n\nprint ("calculating accuracy_score on train data.")\nstart_time = time.time()\nscore = accuracy_score(train_y, train_y_predicted)\nprint ("--- time to calcualte accuracy_score %s seconds ---" % (time.time() - start_time))\nprint ("type(score)=", type(score), "score=", score )\n\n#for i in range(len(predicted)):\n\nprint ("clf.predicting & writing to file")\nstart_time = time.time()\nwith open(loc_submission, "w") as outfile:\n    outfile.write("Id,Cover_Type\\\n")\n    for e, val in enumerate(list(clf.predict(X_test))):\n        outfile.write("%s,%s\\\n" % (test_ids[e], val))\nprint("--- time to clf.predict & write to file %s seconds ---" % (time.time() - start_time))\n\n\''}, {'role': 'assistant', 'content': "['helper_functions']"}]