# On the existence of rule of thumb for machine learning algorithms

I want to know if there are conditions about the minimum number of observations to have (the relation between the number of variables and the number of presence and absence records) in order to use the following machine learning algorithms: CART, RFs, and BRI (I will be very thankful if there are any references).

There are a few rules of thumb found in Frank Harrell's Regression Modelling Strategies. If $$p$$ is the number of variables in the model, then the rule of thumb is to use between $$p=m/10$$ and $$p=m/20$$ variables. here $$m$$ is the "limiting sample size" according to Frank. When the outcome is continuous then the $$m$$ is the total number of observations. When the outcome is binary, $$m = \min(n_1, n_0)$$ where $$n_1, n_0$$ are the counts cases where the outcome was 1 and 0 respectively.