Hands-On Machine Learning for Algorithmic Trading

Regression problems

Regression problems aim to predict a continuous variable. The root-mean-square error (RMSE) is the most popular loss function and error metric, not least because it is differentiable. The loss is symmetric, but larger errors weigh more in the calculation. Using the square root has the advantage of measuring the error in the units of the target variable. The same metric in combination with the RMSE log of the error (RMSLE) is appropriate when the target is subject to exponential growth because of its asymmetric penalty that weights negative errors less than positive errors. You can also log-transform the target first and then use the RMSE, as we do in the example later in this section.

The mean absolute errors (MAE) and ...