Chi-Squared test: ok for selecting significant features? I would have a question on the contingency table and its results.
I was performing this analysis on names starting with symbols as a possible feature, getting the following values:
Label          0.0  1.0     
with_symb      1584 241
without_symb     16 14

getting a p-value which lets met conclude that variables are associated (since it is less than 0.05).
My question is if this result might be a good result based on the chi-squared test, so if I can include in the model.
I am selecting individually features to enter the model based on the chi-squared. Maybe there is another way to select the most appropriate and significant features for the model.
Any suggestions on this would be great.
I will raise several issues that could arise if you are selecting features based on chi-2 tests

Repeated use of chi-2 test can lead to spurious results unless you correct for the number of times you run it

You can include features that are correlated with each other, i.a. A is correlated with B, and both are correlated with label. Not sure, but I think, this can lead to results where model performs worse with more features.


I would try starting with all the features, remove the ones linearly correlated.  But this is just a suggestion.
Also, mutual information can be used to estimate how well any given feature describes the label.