When using Neural Networks for image processing I learned a rule of thumb: to avoid overfitting, supply at least 10 training examples for every neuron.
Is there a similar rule of thumb for classifiers such as
XGBoost, presumably taking into account the number of features and estimators?
And, considering the 'curse of dimensionality' shouldn't the rule of thumb be that
n_training is geometric in
n_dimensions, and not linear?