Hands-On Machine Learning for Algorithmic Trading

Learning curves

The learning curve (see the right-hand panel of the preceding chart for our house price regression example) helps determine whether a model's cross-validation performance would benefit from additional data and whether prediction errors are more driven by bias or by variance.

If training and cross-validation performance converges, then more data is unlikely to improve the performance. At this point, it is important to evaluate whether the model performance meets expectations, determined by a human benchmark. If this is not the case, then you should modify the model's hyperparameter settings to better capture the relationship between the features and the outcome, or choose a different algorithm with a higher capacity to capture ...