{"action":"opened","issue":{"url":"https://api.github.com/repos/JuliaML/MLDataUtils.jl/issues/27","repository_url":"https://api.github.com/repos/JuliaML/MLDataUtils.jl","labels_url":"https://api.github.com/repos/JuliaML/MLDataUtils.jl/issues/27/labels{/name}","comments_url":"https://api.github.com/repos/JuliaML/MLDataUtils.jl/issues/27/comments","events_url":"https://api.github.com/repos/JuliaML/MLDataUtils.jl/issues/27/events","html_url":"https://github.com/JuliaML/MLDataUtils.jl/issues/27","id":213260529,"number":27,"title":"Naming Convention for FeatureNormalizer","user":{"login":"asbisen","id":5142211,"avatar_url":"https://avatars1.githubusercontent.com/u/5142211?v=3","gravatar_id":"","url":"https://api.github.com/users/asbisen","html_url":"https://github.com/asbisen","followers_url":"https://api.github.com/users/asbisen/followers","following_url":"https://api.github.com/users/asbisen/following{/other_user}","gists_url":"https://api.github.com/users/asbisen/gists{/gist_id}","starred_url":"https://api.github.com/users/asbisen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/asbisen/subscriptions","organizations_url":"https://api.github.com/users/asbisen/orgs","repos_url":"https://api.github.com/users/asbisen/repos","events_url":"https://api.github.com/users/asbisen/events{/privacy}","received_events_url":"https://api.github.com/users/asbisen/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-03-10T06:57:05Z","updated_at":"2017-03-10T06:57:05Z","closed_at":null,"body":"`FeatureNormalizer` transforms the matrix `X` using `(X - μ)/σ` which translates to `StandardScaler` in Scikit-Learn. Whereas `Normalize` method in Scikit-Learn scales the data to a unit norm. I was wondering if we should rename `FeatureNormalizer` to `FeatureStandardizer` or something to that effect. \r\n\r\nAlso is there a reason for having `FeatureNormalizer` to expect the Matrix such that the features are represented in `rows` and not `columns`\r\n\r\nAnd for the last issue I don't know which is correct way `Scikit` or `MLDataUtils`. But there is a slight inconsistency between how `StandardScaler` in `Scikit` calculates standard deviation vs `MLDataUtils`. With `Scikit` they use `n` to scale the sum and we use `n-1` to scale the sum while calculating standard deviation.\r\n\r\n\r\n[Reference: Scikit-Learn Standardize](http://scikit-learn.org/stable/modules/preprocessing.html#standardization-or-mean-removal-and-variance-scaling)\r\n[Reference: Scikit-Learn Normalize](http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing-normalization) "}}
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