Financial-modeling

In traditional settings, cross-validation is an effective tool for evaluating a machine learning model's performance. However, the complexities of financial data pose unique challenges.

When dealing with a set of training observations and outcomes, you would want to estimate a function that closely matches the true function.

Hyper-parameter tuning is crucial for optimizing machine learning (ML) algorithms. Effective tuning results in improved real-world performance.