As others have said, pregnant men, while uncommon, might appear in your dataset. However, for preliminary results, it could make sense to exclude the possibility. Depending on the prevalence and impact of pregnancy in your data, you may be free to drop the column entirely. Some exploratory data analysis should help. Pregnancy should be relatively frequent (>5% of rows, at the very least). Pregnant women should also have noticably different target statistics than non-pregnant women. If pregnancy is rare or doesn’t seem to have much of an impact, feel free to drop it altogether for now.
Alternatively, I would default to basic indicator columns for both sex and pregnancy. Just be sure your model doesn’t require feature independence, as some do.
As others have said, pregnant men, while uncommon, might appear in your dataset. However, for preliminary results, it could make sense to exclude the possibility. Depending on the prevalence and impact of pregnancy in your data, you may be free to drop the column entirely. Some exploratory data analysis should help. Pregnancy should be relatively frequent (>5% of rows, at the very least). Pregnant women should also have noticably different target statistics than non-pregnant women. If pregnancy is rare or doesn’t seem to have much of an impact, feel free to drop it altogether for now.
Alternatively, I would default to basic indicator columns for both sex and pregnancy. Just be sure your model doesn’t require feature independence, as some do.