{"action":"opened","issue":{"url":"https://api.github.com/repos/tqchen/xgboost/issues/166","labels_url":"https://api.github.com/repos/tqchen/xgboost/issues/166/labels{/name}","comments_url":"https://api.github.com/repos/tqchen/xgboost/issues/166/comments","events_url":"https://api.github.com/repos/tqchen/xgboost/issues/166/events","html_url":"https://github.com/tqchen/xgboost/issues/166","id":56732253,"number":166,"title":"Guess if the presence or the absence of a feature helps to be in a specific class ","user":{"login":"pommedeterresautee","id":1029874,"avatar_url":"https://avatars.githubusercontent.com/u/1029874?v=3","gravatar_id":"","url":"https://api.github.com/users/pommedeterresautee","html_url":"https://github.com/pommedeterresautee","followers_url":"https://api.github.com/users/pommedeterresautee/followers","following_url":"https://api.github.com/users/pommedeterresautee/following{/other_user}","gists_url":"https://api.github.com/users/pommedeterresautee/gists{/gist_id}","starred_url":"https://api.github.com/users/pommedeterresautee/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pommedeterresautee/subscriptions","organizations_url":"https://api.github.com/users/pommedeterresautee/orgs","repos_url":"https://api.github.com/users/pommedeterresautee/repos","events_url":"https://api.github.com/users/pommedeterresautee/events{/privacy}","received_events_url":"https://api.github.com/users/pommedeterresautee/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-02-05T21:26:56Z","updated_at":"2015-02-05T21:26:56Z","closed_at":null,"body":"Each predictor improves the purity of the branch it is on. The purity is measured by the gain.\r\n\r\nIn a binary classification, some features need to be present in an observation to predict that the observation will be classified as 1 and some other predictors need to be absent to get the exact same result.\r\n\r\nMy question is how, **based on the model text dump of a binary classification**, can I say that the presence of a feature A helps to predict that an observation will be classified as 1 and another feature B absence helps to predict that an observation will be classified as 1.\r\n\r\nmodel may look like that\r\n```R\r\n> xgb.dump(bst, with.stats = T)\r\n [1] \"booster[0]\"                                                     \r\n [2] \"0:[f28<1.00001] yes=1,no=2,missing=2,gain=4000.53,cover=1628.25\"\r\n [3] \"1:[f108<1.00001] yes=3,no=4,missing=4,gain=198.174,cover=703.75\"\r\n [4] \"3:leaf=1.85965,cover=13.25\"                                     \r\n [5] \"4:leaf=-1.94071,cover=690.5\"                                    \r\n [6] \"2:[f55<1.00001] yes=5,no=6,missing=6,gain=1158.21,cover=924.5\"  \r\n [7] \"5:leaf=-1.70044,cover=112.5\"                                    \r\n [8] \"6:leaf=1.71218,cover=812\"                                       \r\n [9] \"booster[1]\"                                                     \r\n[10] \"0:[f59<1.00001] yes=1,no=2,missing=2,gain=832.545,cover=788.852\"\r\n[11] \"1:leaf=-6.23624,cover=20.4624\"                                  \r\n[12] \"2:[f28<1.00001] yes=3,no=4,missing=4,gain=569.725,cover=768.39\" \r\n[13] \"3:leaf=-0.96853,cover=309.453\"                                  \r\n[14] \"4:leaf=0.784718,cover=458.937\"   \r\n```"}}