I'm currently working on a logistic regression model for genomics. One of the input fields I want to include as a covariate is
genes. There are around 24,000 known genes. There are many features with this level of variability in computational biology and hundreds of thousands of samples are needed.
- If I
LabelEncoder()those 24K genes
- and then
Is 24,000 columns going to make my keras training times unreasonable for a 2.2 GHz quad-core i7 CPU?
If so, is there a different approach to encoding that I can take with this?
Should I somehow try to dedicate a layer of my model to this feature?
Does this mean I need 24K input nodes?