Easy Access To ML Hyperparams for Optimization 🚀
Supports Sklearn, LightGBM, XGBoost, and CatBoost
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{
"loss": "squared_error",
"penalty": "l2",
"alpha": 0.0001,
"l1_ratio": 0.15,
"fit_intercept": True,
"max_iter": 1000,
"tol": 0.001,
"shuffle": True,
"verbose": 0,
"epsilon": 0.1,
"random_state": None,
"learning_rate": "invscaling",
"eta0": 0.01,
"power_t": 0.25,
"early_stopping": False,
"validation_fraction": 0.1,
"n_iter_no_change": 5,
"warm_start": False,
"average": False
}
This is a simple tool made by a data scientist for data scientists that train machine learning models.
It just helps you access the hyperparameters of the model you want to use in a
fast and efficient way.
It's not a replacement for the documentation. It's just a way to quickly get the hyperparameters you
need to optimize your model without having to look online for them. All parameter grids are manually
curated and updated.
A list of the most relevant libraries and models