Learning curves are important for decision making in supervised machine learning. They show how the performance of a machine learning model develops over a given resource. In this work, we consider...Show moreLearning curves are important for decision making in supervised machine learning. They show how the performance of a machine learning model develops over a given resource. In this work, we consider learning curves that model the performance of a machine learning model as a function of the number of data points used for training. For decision making, it is of- ten useful to extrapolate learning curves, which can be done, for example, by fitting a parametric model based on the observed values, or by using an extrapolation model trained on learning curves from similar datasets. We perform an analysis comparing these two techniques with different ob- servations and prediction objectives. When only a small number of initial segments of the learning curve have been observed we find that it is better to rely on learning curves from similar datasets. Once more observations have been made, a parametric model, or just the last observation, should be used. Moreover, we find that using a parametric model is mostly use- ful when the exact value of the learning curve itself is of interest. Lastly, we use this knowledge to improve machine learning on a particle physics dataset.Show less
