tensorflow / tensorflow/probability
Categorical features in logistic regression
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Description
Hi,
I'm following the logistic regression example (https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/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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Research direction
The issue points to tensorflow_probability/examples/logistic_regression.py; read that example first and compare its treatment of inputs with the reported mixed numerical and categorical data. The request does not define a specific implementation, file change, test, or completion criterion, so scope and “done” would need maintainer clarification.
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Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100