{"action":"opened","issue":{"url":"https://api.github.com/repos/haantran96/ML-Week-2/issues/1","repository_url":"https://api.github.com/repos/haantran96/ML-Week-2","labels_url":"https://api.github.com/repos/haantran96/ML-Week-2/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/haantran96/ML-Week-2/issues/1/comments","events_url":"https://api.github.com/repos/haantran96/ML-Week-2/issues/1/events","html_url":"https://github.com/haantran96/ML-Week-2/issues/1","id":326098796,"number":1,"title":"Thanks!","user":{"login":"coderschoolreview","id":15702670,"avatar_url":"https://avatars3.githubusercontent.com/u/15702670?v=4","gravatar_id":"","url":"https://api.github.com/users/coderschoolreview","html_url":"https://github.com/coderschoolreview","followers_url":"https://api.github.com/users/coderschoolreview/followers","following_url":"https://api.github.com/users/coderschoolreview/following{/other_user}","gists_url":"https://api.github.com/users/coderschoolreview/gists{/gist_id}","starred_url":"https://api.github.com/users/coderschoolreview/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/coderschoolreview/subscriptions","organizations_url":"https://api.github.com/users/coderschoolreview/orgs","repos_url":"https://api.github.com/users/coderschoolreview/repos","events_url":"https://api.github.com/users/coderschoolreview/events{/privacy}","received_events_url":"https://api.github.com/users/coderschoolreview/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-05-24T12:28:42Z","updated_at":"2018-05-24T12:28:42Z","closed_at":null,"author_association":"NONE","body":"**Assignment 2**\r\n\r\nThe goal of this assignment was to introduce you to three new classification techniques and to understand how to select the best parameters and features for them. You learned how to use python built-in functions (GridSearchCV, SelectKBest, RFE, SelectFromModel) to try out new models (Support Vector Machines, Random Forests, and Logistic Regression) and test different permutations of parameter values and features, and analyze your results to help build better machine learning models.\r\n\r\nYou are obviously a good student and quite smart! So it's very unfortunate that you did not complete this assignment properly. Here are the missing sections:\r\n\r\n* You are missing the confusion matrix and classification report for all the best classifiers\r\n* Completely missing the optimized classifiers for `SVC` (did not run `SelectKBest`) and `LogisticRegression` (did not run`SelectFromModel` and `RFE`)\r\n* You did not discuss any results. It is important to talk about your results and evaluate them so you can demonstrate that you are understanding the concepts being studied. Example:\r\n    * How did the optimized classifiers work compared to the original and best?\r\n    * Did `GridSearchCV` always give you good results? Why? Or why not?\r\n\r\nThe parts that you did complete are quite good, but unfortunately you need to do a better job on the next assignment. I know that you had a work trip this week and so you were unable to start your assignment on time. But you are a good student and have a lot of potential! So please make sure to complete all the required user stories next time. Thanks!\r\n"}}
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