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 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?
It's completely true that the number of examples should be related to features. But it is not only the number of features because the range of a number(max-min and count of different number) is also important. On the other hand, if you have noise you need more examples, so it's related to your dataset.