# Use distribution probability as a feature in ML model

I built an LSMT model to predict sick cows. I also have risk factors like cow size and height (static risk factor) that I want to combine into the ML model. I found that size is geometrically distributed. My question is how I insert it as a feature to the model? I know that $$P(x=K)= p*q^(k-1)$$ but I don't know how to combine it as a feature. Thank you.

• Are you sure you want to insert a theoretical distribution instead of the measured value for each cow? – Dave Apr 20 at 11:43

## 1 Answer

As a general approach I would say you need to generate new features, that use your prior knowledge. For example, if you have a known size distribution, then for each specific size you can calculate its probability and use it as a new feature.

As I side-note, the geometric distribution of cow sizes seems very surprising to me, I would expect to see some gamma distribution or just normal (if size is measured in cm/inches).

• I just not understand how to calcalute it. for example if it is geometric so for cow at size 50 the formula is $P(x=K)= p*q^(k-1)$ , how I can get P? – Mor Apr 21 at 10:46
• It's a distribution parameter which you can estimate by training data with methods like maximum likelihood estimation. "I found that size is geometrically distributed" -- could you please add to question the explanation, how did you find that and may be example of the data. – Kirill Fedyanin Apr 21 at 10:56