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Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.

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Bumps [pillow](https://github.com/python-pillow/Pillow) from 10.0.0 to 10.0.1. Release notes Sourced from pillow's releases. 10.0.1 https://pillow.readthedocs.io/en/stable/releasenotes/10.0.1.html Changes Updated libwebp to 1.3.2 #7395 [@​radarhere] Updated zlib to 1.3 #7344 [@​radarhere] Changelog Sourced from...

dependencies

### Complete Description of the Issue As a software developer, I want to tune dict-like parameters of ML model to improve its ability to generalize. This is a default behavior...

### Complete Description of the Issue As a software developer, I want to track the improvement of ML model over iterations using multiple scoring metrics. This is a default behavior...

Hi, Can I have a help here please I am using this NatureInspiredSearchCV as ``` grid = NatureInspiredSearchCV(model, cv=3, param_grid=model_parameters_space, verbose=0, algorithm='hba', population_size=50, max_n_gen=100, max_stagnating_gen=20, runs=5, scoring='accuracy', # n_jobs=-1, random_state=42)...