Feature/tuner multiple model #461
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Implemented to allow running many model configurations in the Tuner. Allowing you to run different models, with each of them having their own search_spaces. A new Notebook tutorial was also created demonstrating how to use and a use case in which this new feature can be useful.
The output of the Tuner with multiple models will be a Dataframe with all models, hyperparameters and metrics results, facilitating comparison, including the best model that can be saved on disk.
For more details, please check the 15-Search Best Architecture and Hyperparameter Notebook.
PS: Numpy >= 2.0.0 is raising the following error:
"AttributeError:
np.float_was removed in the NumPy 2.0 release. Usenp.float64instead."Added importlib_metadata fixed version (pypa/twine#977) to fix the following error raised in PR check:
