Hyperparameter tuning with sktime
In previous recipes, you trained different regressors using their default hyperparameter values—parameters that define the model’s structure and behavior but are not learned from the data (they are supplied before training the model). Fine-tuning these hyperparameters can significantly improve model performance by optimizing settings to better fit the data considered, a process known as hyperparameter tuning.In this recipe, you will use sktime’s ForecastingGridSearchCV to perform hyperparameter tuning through cross-validated grid search to find the optimal combination of parameter values. The sktime library also provides other hyperparameter tuning options, such as ForecastingRandomizedSearchCV, ForecastingSkoptSearchCV, and ForecastingOptunaSearchCV, offering different approaches to hyperparameter optimization.Generally, in grid search, we define a grid of hyperparameters—a range of values we want to try for each parameter...
