# Minimum number of samples to train XGBoost without overfitting

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 XGBoost, presumably taking into account the number of features and estimators?

• Or by other parameters that reduce overfitting of XGBoost, for example minimum weight to create split (min_child_weight in XGBoost package in Python). – Itamar Mushkin Jan 19 at 6:19