| Name | Version | Summary | date |
| random-forest-mc |
1.1.1 |
This project is about use Random Forest approach using a dynamic tree selection Monte Carlo based. |
2024-03-17 17:19:48 |
| DumME |
0.1.0 |
Mixed Effects Dummy Model |
2024-01-30 08:08:33 |
| tulia |
0.2.1 |
numpy based machine learning package with sklearn-like API |
2024-01-12 03:38:17 |
| treesmoothing |
0.0.3 |
Bayesian post-hoc regularization for random forests |
2023-12-20 11:43:46 |
| autopilotml |
1.0.5 |
A package for automating machine learning tasks |
2023-11-23 20:20:08 |
| miceforest |
5.7.0 |
Missing Value Imputation using LightGBM |
2023-11-16 02:24:08 |
| randomForestUltra |
0.5.0 |
2023-11-09 11:23:56It supports multi-objective variable and multi-fold random forest, and can calculate P value through random permutation. |
2023-11-09 03:25:37 |
| mafese |
0.1.9 |
MAFESE: Metaheuristic Algorithm for Feature Selection - An Open Source Python Library |
2023-08-07 11:14:05 |
| fgclustering |
1.0.3 |
Forest-Guided Clustering - Explainability method for Random Forest models. |
2023-04-12 16:38:13 |
| AdvancedAnalytics |
1.39 |
Python support for 'The Art and Science of Data Analytics' |
2023-03-31 00:09:38 |
| nsforest |
3.9.2.5 |
A machine learning method for the discovery of minimum marker gene combinations for cell type identification from single-cell RNA sequencing |
2023-03-17 06:29:34 |
| prfr |
0.2.4 |
Probabilitic random forest regression algorithm |
2023-01-25 05:54:07 |
| cubrif |
1.4.3 |
Build random forests using CUDA GPU. |
2023-01-15 01:04:34 |
| brif |
1.4.3 |
Build decision trees and random forests for classification and regression. |
2023-01-14 22:31:30 |
| Boruta |
0.3 |
Python Implementation of Boruta Feature Selection |
2019-05-20 07:47:00 |