Python for Finance Cookbook

Tuning hyperparameters using grid searches and cross-validation

Cross-validation, together with grid search, is commonly used to tune the hyperparameters of the model in order to achieve better performance. Below, we outline the differences between hyperparameters and parameters.

Hyperparameters:

• External characteristic of the model
• Not estimated based on data
• Can be considered the model's settings
• Set before the training phase
• Tuning them can result in better performance

Parameters:

• Internal characteristic of the model
• Estimated based on data, for example, the coefficients of linear regression
• Learned during the training phase

One of the challenges of machine learning is training models that are able to generalize well to unseen data ...