{"action":"created","issue":{"url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/1","repository_url":"https://api.github.com/repos/JianWenJun/MLDemo","labels_url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/1/comments","events_url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/1/events","html_url":"https://github.com/JianWenJun/MLDemo/issues/1","id":348614098,"node_id":"MDU6SXNzdWUzNDg2MTQwOTg=","number":1,"title":"The efficiency of the model","user":{"login":"sdcslyz","id":32164352,"node_id":"MDQ6VXNlcjMyMTY0MzUy","avatar_url":"https://avatars1.githubusercontent.com/u/32164352?v=4","gravatar_id":"","url":"https://api.github.com/users/sdcslyz","html_url":"https://github.com/sdcslyz","followers_url":"https://api.github.com/users/sdcslyz/followers","following_url":"https://api.github.com/users/sdcslyz/following{/other_user}","gists_url":"https://api.github.com/users/sdcslyz/gists{/gist_id}","starred_url":"https://api.github.com/users/sdcslyz/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sdcslyz/subscriptions","organizations_url":"https://api.github.com/users/sdcslyz/orgs","repos_url":"https://api.github.com/users/sdcslyz/repos","events_url":"https://api.github.com/users/sdcslyz/events{/privacy}","received_events_url":"https://api.github.com/users/sdcslyz/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":4,"created_at":"2018-08-08T07:52:51Z","updated_at":"2018-08-08T09:32:24Z","closed_at":null,"author_association":"NONE","body":"After using your model and all of your data set, I run your code and utilize all features that u extracted. However, only 0.74 accuracy and 0.47 F1-score I get. I feel puzzled by your efficiency. Could u explain about it to me? Thanks. "},"comment":{"url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/comments/411346213","html_url":"https://github.com/JianWenJun/MLDemo/issues/1#issuecomment-411346213","issue_url":"https://api.github.com/repos/JianWenJun/MLDemo/issues/1","id":411346213,"node_id":"MDEyOklzc3VlQ29tbWVudDQxMTM0NjIxMw==","user":{"login":"JianWenJun","id":15305308,"node_id":"MDQ6VXNlcjE1MzA1MzA4","avatar_url":"https://avatars2.githubusercontent.com/u/15305308?v=4","gravatar_id":"","url":"https://api.github.com/users/JianWenJun","html_url":"https://github.com/JianWenJun","followers_url":"https://api.github.com/users/JianWenJun/followers","following_url":"https://api.github.com/users/JianWenJun/following{/other_user}","gists_url":"https://api.github.com/users/JianWenJun/gists{/gist_id}","starred_url":"https://api.github.com/users/JianWenJun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/JianWenJun/subscriptions","organizations_url":"https://api.github.com/users/JianWenJun/orgs","repos_url":"https://api.github.com/users/JianWenJun/repos","events_url":"https://api.github.com/users/JianWenJun/events{/privacy}","received_events_url":"https://api.github.com/users/JianWenJun/received_events","type":"User","site_admin":false},"created_at":"2018-08-08T09:32:24Z","updated_at":"2018-08-08T09:32:24Z","author_association":"OWNER","body":"@sdcslyz in my computer, I use the method get 0.803 accuracy and 0.492 F1-score. the code was submitted Official test platform get 0.6238 F1-score. the reason may be is that the training data set has grown larger and extracted depth model features cannot use predictive results as input to the final classifier. Here you can consider the hidden layer output of the extracted depth model as the input feature of the final classifier."}}
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