| Name | Version | Summary | date |
| significance_analysis |
0.2.4 |
Significance Analysis for HPO-algorithms performing on multiple benchmarks |
2024-11-01 13:39:32 |
| significance-analysis |
0.2.1 |
Significance Analysis for HPO-algorithms performing on multiple benchmarks |
2024-09-11 07:05:30 |
| search-in-a-third |
0.1.1 |
A Python package for efficient hyperparameter optimization in neural networks, using a greedy algorithm guided by heuristic directions. |
2024-04-01 10:38:06 |
| TPOT |
0.12.2 |
Tree-based Pipeline Optimization Tool |
2024-02-23 19:06:12 |
| hpo4dl |
0.1.0 |
Hyper parameter optimization for deep learning |
2023-10-06 12:15:33 |
| gecs |
0.1.1 |
LightGBM Classifier with integrated bayesian hyperparameter optimization |
2023-09-10 09:29:08 |
| autoqtl |
0.1.1a0 |
Automated Quantitative Trait Locus Analysis Tool |
2023-08-17 16:47:28 |
| ts-hyperparam-opt |
0.1.3 |
Convenience package for parallelized hyperparameter optimization (e.g. in Jupyter Notebooks) using grid search and CV |
2023-03-26 21:00:35 |
| jahs-bench |
1.1.0 |
The first collection of surrogate benchmarks for Joint Architecture and Hyperparameter Search. |
2023-03-22 14:52:02 |
| tscv |
0.1.3 |
Time series cross-validation |
2023-01-23 18:50:02 |