{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f94cab24-3316-e0a8-1766-ed7c5659dcf4"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport xgboost as xgb\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.\ndtrain = xgb.DMatrix('../input/clicks_train.csv')\ndtest = xgb.DMatrix('../input/clicks_test.csv')\n\nparam = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'binary:logistic' }\nparam['nthread'] = 4\nparam['eval_metric'] = 'auc'\n\nevallist  = [(dtest,'eval'), (dtrain,'train')]"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0}