Consider a different scenario: an unknown nonlinear dynamical system is encapsulated into a blackbox and only an ensemble of trajectories (input and output data) are available. Comparing actual trajectories to interpolated trajectories is one way to gauge chaos. A typical method to interpolate from known trajectories is to build a surrogate model with machine learning techniques. A surrogate model needs to be established first, then predictions can be made by evaluating trajectories with given initial conditions. This procedure is known as "supervised learning" [2]. To validate the model, the **data** is often randomly split into two clusters: a large training set and a small testing set. A model is then constructed from the training set. The performance of the model, i.e., the prediction accuracy, is measured by comparing the testing **data** against its prediction. The performance of the model depends on the type and complexity of the model, the volume of training data, the algorithm used for training, etc.