With algorithms that, for each additional input entry, either add a predictable amount of complexity to improve fitness (e.g. another tree split) or decide that

With algorithms that, for each additional input entry, either add a predictable amount of complexity to improve fitness (e.g. another tree split) or decide that the model is already fit enough, one needs to do something deliberate or very stupid to overfit data compare to neural networks where overfitting is hard to detect, let alone prevent quantitatively, and that are sometimes designed to overfit in order to interpolate "intelligently" between samples.