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GitHub / mghasemi / sksurrogate
SKSurrogate is a suite of tools that implements surrogate optimization for expensive functions based on scikit-learn. The main purpose of SKSurrogate is to facilitate hyperparameter optimization for machine learning models and optimized pipeline design (AutoML).
JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mghasemi%2Fsksurrogate
Stars: 1
Forks: 2
Open Issues: 0
License: mit
Language: Python
Repo Size: 2.3 MB
Dependencies:
28
Created: over 5 years ago
Updated: 9 months ago
Last pushed: 9 months ago
Last synced: 9 months ago
Topics: automatic-machine-learning, automl, classification, eoa, hyperparameter-optimization, machine, machine-learning, model-selection, optimization, regression, surrogate-based-optimization
Files
Dependencies
- SALib *
- category-encoders *
- eli5 *
- imbalanced-learn *
- joblib *
- lightgbm *
- matplotlib *
- numpy *
- pandas *
- peewee *
- scikit-learn *
- scipy *
- six *
- skrebate *
- tqdm *
- SALib *
- category_encoders *
- eli5 *
- imbalanced-learn *
- joblib *
- matplotlib *
- numpy *
- pandas *
- peewee *
- scikit-learn *
- scipy *
- skrebate *
- tqdm *