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I need some feedback on a problem I have been working on. I am working with a fairly balanced dataset with all categorical features, and a categorical outcome (classification problem). The data has no continuous numerical features. To predict my outcome on testset I am using xgboost algorithm. Since I have all categorical predictors I am using one-hot encoding to handle my categorical features. Now I am a bit worried that I might be missing something in the process, so I wanted to check if I have all categorical features with a binary outcome is this a valid approach? I don't see any other way to deal with this problem.

FYI the categorical variables are not things like ZIP codes, IDs...they are actually relevant to the outcome...e.g. smoker (yes/no) | high bp (yes/no)

What do you think?

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  • $\begingroup$ What would you do with a continuous response? $\endgroup$
    – Dave
    Jun 13 at 23:56
  • $\begingroup$ yes it wont help, and it doesn't make sense either. I was just thinking something aloud.. :) $\endgroup$ Jun 14 at 0:14
  • $\begingroup$ But what would you do if you had these categorical features and wanted to predict a continuous quantity? $\endgroup$
    – Dave
    Jun 14 at 0:42
  • $\begingroup$ I mean you can still use the same approach, only the nature of the problem changes a bit. From classification to regression. With one caveat that you might have to scale or normalize a few things. The base case logistic regression with no continuous variable comes to mind $\endgroup$ Jun 14 at 0:47

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I don't see any problem doing classification with purely categorical features, as far as the features are relevant.

And as always, some precautions dealing with categorical features:

  1. The choice of model. Some models can handle categorical features off-the-shelf (e.g. tree-based algorithm), some are specifically designed for (e.g. CatBoost). These models may ease your feature engineering work, and probably better accuracy.

  2. Cardinality. Sometimes a categorical feature can take a lot of values (plus unknown/unseen ones), which can be a problem. You should think ahead about what to do in these cases.

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  • $\begingroup$ yes what you said is true. I have been running xgboost, and catboost on my data to see how they perform. Thankfully the cardinality is fairly low in my data - so I got lucky there. Thanks $\endgroup$ Jun 14 at 16:56

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