LightGBM Tuner: New Optuna Integration for Hyperparameter Optimization
In this article, we will introduce the LightGBM Tuner in Optuna, a hyperparameter optimization framework, particularly designed for machine learning. The LightGBM Tuner is one of Optuna’s integration modules for optimizing hyperparameters of LightGBM. The usage of LightGBM Tuner is straightforward. You use LightGBM Tuner by changing one import statement in your Python code. Using expert heuristics
Keras documentation: KerasTuner
KerasTuner Star KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algori
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[Tutorial] Bayesian Optimization with LightGBM
Bayesian optimization with skopt — scikit-optimize 0.8.1 documentation
GitHub - scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface
Understanding UMAP
Matei Zaharia
https://scikit-learn.org/stable/modules/model_evaluation.html
GitHub - benhamner/Metrics: Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
Custom losses - PyTorch Metric Learning