{"action":"closed","issue":{"url":"https://api.github.com/repos/tensorflow/probability/issues/94","repository_url":"https://api.github.com/repos/tensorflow/probability","labels_url":"https://api.github.com/repos/tensorflow/probability/issues/94/labels{/name}","comments_url":"https://api.github.com/repos/tensorflow/probability/issues/94/comments","events_url":"https://api.github.com/repos/tensorflow/probability/issues/94/events","html_url":"https://github.com/tensorflow/probability/issues/94","id":338610714,"node_id":"MDU6SXNzdWUzMzg2MTA3MTQ=","number":94,"title":"Categorical features in logistic regression","user":{"login":"allenchng","id":31014457,"node_id":"MDQ6VXNlcjMxMDE0NDU3","avatar_url":"https://avatars1.githubusercontent.com/u/31014457?v=4","gravatar_id":"","url":"https://api.github.com/users/allenchng","html_url":"https://github.com/allenchng","followers_url":"https://api.github.com/users/allenchng/followers","following_url":"https://api.github.com/users/allenchng/following{/other_user}","gists_url":"https://api.github.com/users/allenchng/gists{/gist_id}","starred_url":"https://api.github.com/users/allenchng/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/allenchng/subscriptions","organizations_url":"https://api.github.com/users/allenchng/orgs","repos_url":"https://api.github.com/users/allenchng/repos","events_url":"https://api.github.com/users/allenchng/events{/privacy}","received_events_url":"https://api.github.com/users/allenchng/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-07-05T14:57:16Z","updated_at":"2018-07-16T14:08:55Z","closed_at":"2018-07-16T14:08:55Z","author_association":"NONE","body":"Hi, \r\n\r\nI'm following the logistic regression example ([https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/logistic_regression.py](logistic_regression.py)) and trying to implement it with some real life data that has a mixture of numerical and categorical variables. Each of my categorical variables has a large number of elements though, and I'm wondering if one hot encoding is the best approach. Is there a better way of treating my categorical variables in TF/TF-p? Thanks. "}}
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