pycaret.time_series.tune_model#

pycaret.time_series.tune_model(estimator, fold: int | Any | None = None, round: int = 4, n_iter: int = 10, custom_grid: Dict[str, list] | Any | None = None, optimize: str = 'MASE', custom_scorer=None, search_algorithm: str | None = None, choose_better: bool = True, fit_kwargs: dict | None = None, return_tuner: bool = False, verbose: bool = True, tuner_verbose: int | bool = True, **kwargs)[source]#

This function tunes the hyperparameters of a given estimator. The output of this function is a score grid with CV scores by fold of the best selected model based on optimize parameter. Metrics evaluated during CV can be accessed using the get_metrics function. Custom metrics can be added or removed using add_metric and remove_metric function.

Example

>>> from pycaret.datasets import get_data
>>> airline = get_data('airline')
>>> from pycaret.time_series import *
>>> exp_name = setup(data = airline,  fh = 12)
>>> dt = create_model('dt_cds_dt')
>>> tuned_dt = tune_model(dt)

estimator: sktime compatible object: Trained model object
fold: int or scikit-learn compatible CV generator, default = None: Controls cross-validation. If None, the CV generator in the fold_strategy parameter of the setup function is used. When an integer is passed, it is interpreted as the ‘n_splits’ parameter of the CV generator in the setup function.
round: int, default = 4: Number of decimal places the metrics in the score grid will be rounded to.
n_iter: int, default = 10: Number of iterations in the grid search. Increasing ‘n_iter’ may improve model performance but also increases the training time.
custom_grid: dictionary, default = None: To define custom search space for hyperparameters, pass a dictionary with parameter name and values to be iterated. Custom grids must be in a format supported by the defined search_library.
optimize: str, default = ‘MASE’: Metric name to be evaluated for hyperparameter tuning. It also accepts custom metrics that are added through the add_metric function.
custom_scorer: object, default = None: custom scoring strategy can be passed to tune hyperparameters of the model. It must be created using sklearn.make_scorer. It is equivalent of adding custom metric using the add_metric function and passing the name of the custom metric in the optimize parameter. Will be deprecated in future.
search_algorithm: str, default = ‘random’: use ‘random’ for random grid search and ‘grid’ for complete grid search.
choose_better: bool, default = True: When set to True, the returned object is always better performing. The metric used for comparison is defined by the optimize parameter.
fit_kwargs: dict, default = {} (empty dict): Dictionary of arguments passed to the fit method of the tuner.
return_tuner: bool, default = False: When set to True, will return a tuple of (model, tuner_object).
verbose: bool, default = True: Score grid is not printed when verbose is set to False.
tuner_verbose: bool or in, default = True: If True or above 0, will print messages from the tuner. Higher values print more messages. Ignored when verbose param is False.
**kwargs:: Additional keyword arguments to pass to the optimizer.

Returns:: Trained Model and Optional Tuner Object when return_tuner is True.