{"action":"opened","issue":{"url":"https://api.github.com/repos/ga-students/DAT_SF_12/issues/5","labels_url":"https://api.github.com/repos/ga-students/DAT_SF_12/issues/5/labels{/name}","comments_url":"https://api.github.com/repos/ga-students/DAT_SF_12/issues/5/comments","events_url":"https://api.github.com/repos/ga-students/DAT_SF_12/issues/5/events","html_url":"https://github.com/ga-students/DAT_SF_12/issues/5","id":56618222,"number":5,"title":"06 - Neighbors Lab, #2 - Using Scaled Data","user":{"login":"dyerrington","id":2257834,"avatar_url":"https://avatars.githubusercontent.com/u/2257834?v=3","gravatar_id":"","url":"https://api.github.com/users/dyerrington","html_url":"https://github.com/dyerrington","followers_url":"https://api.github.com/users/dyerrington/followers","following_url":"https://api.github.com/users/dyerrington/following{/other_user}","gists_url":"https://api.github.com/users/dyerrington/gists{/gist_id}","starred_url":"https://api.github.com/users/dyerrington/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dyerrington/subscriptions","organizations_url":"https://api.github.com/users/dyerrington/orgs","repos_url":"https://api.github.com/users/dyerrington/repos","events_url":"https://api.github.com/users/dyerrington/events{/privacy}","received_events_url":"https://api.github.com/users/dyerrington/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-02-05T02:19:39Z","updated_at":"2015-02-05T02:19:39Z","closed_at":null,"body":"I'm not sure I'm producing the same outcome we reviewed towards the end class yesterday.  Scaling the data seems like it renders the data useless.\r\n```python\r\n# Load in the data\r\niris = datasets.load_iris()\r\niris_df = pd.DataFrame(scale(iris.data), columns=iris.feature_names)\r\niris_df.describe()\r\n\r\n# etc etc ...\r\n\r\n# Plot also the training points\r\nplt.figure(figsize=(18,6)) # main figure\r\nplt.pcolormesh(xx, yy, Z, cmap=cmap_light) # ??\r\nplt.scatter(iris_df['petal length (cm)'], iris_df['petal width (cm)'], c=iris.target, cmap=cmap_bold) # add plots\r\nplt.xlim(xx.min(), xx.max()) # x scale\r\nplt.ylim(yy.min(), yy.max()) # y scale\r\nplt.title(\"3-Class classification (k = {}, weights = '{}')\".format(clf.n_neighbors, clf.weights)) # add format\r\n```\r\n\r\n![alt text](http://snag.gy/50VbX.jpg \"Everything is red..\")\r\n\r\nAm I scaling in the right place?\r\n"}}