I have a doubt about what will be the right way to use or represent categorical variables with only two values like "sex". I have checked it up from different sources, but I was not able to find any solid reference. For example, if I have the variable sex I usually see this in this form:

id sex 1 male 2 female 3 female 4 male

So I found that one can use dummy variables like this: enter image description here


and also in this way:

enter image description here


Therefore, which one would be more adequate way to deal with this variable, for example, in a classification system. I am inclined to go with the dummy variables, but I would like some opinion about it.



2 Answers 2


This case can be simplified with a single boolean feature because the original variable sex is binary: it can only have values male or female.

This implies that the two values are complementary of each other, so there is no need to keep both: $X_1$ contains exactly as much information as keeping both sex_male and sex_female.

Note that this simplification cannot be done as soon as the categorical variable can have more than two values.

Side note: sex is not always a binary variable anymore, many surveys would propose a third options such as "doesn't identify as binary".

  • $\begingroup$ thank you for your reply, please could you expand your last part in which you mention the third option? $\endgroup$
    – Lila
    Jul 19, 2021 at 16:04
  • $\begingroup$ @Lila this is a recent social change, it's not related to data science: surveys often give several choices, for example 'non binary' additionally to male and female. For example the SO survey asks about gender with many choices: man, woman, "gender queer or gender non-conforming", "non-binary". $\endgroup$
    – Erwan
    Jul 19, 2021 at 20:17

There are three encoding options you can utilise for your scenario of sex(gender)

  1. One hot Encoding: Here each category is mapped to binary variable containing either 0 or 1.Widely utilized when features are nominal.
  2. Dummy Encoding: similar to one hot encoding. While one hot encoding utilises N binary variables for N categories in a variable. Dummy encoding uses N-1 features to represent N labels/categories
  3. Effect Encoding: Also known as deviation encoding or sum encoding. Similar to dummy encoding, however 3 values are used(1,0,-1)

Do look into Encoding Categorical Variables

Do note that gender identity is not always binary(0 or 1)

There are many different gender identities, including male, female, transgender, gender neutral, non-binary, agender, pangender, genderqueer, two-spirit, third gender, and all, none or a combination of these.

  • 1
    $\begingroup$ Some of the text looks similar to analyticsvidhya.com/blog/2020/08/…: "Here each category is mapped to binary variable containing either 0 or 1" vs "Each category is mapped with a binary variable containing either 0 or 1."; "Dummy encoding uses N-1 features to represent N labels/categories." Please make sure to provide proper attribution for your sources. $\endgroup$
    – D.W.
    Jan 1 at 22:38

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.