| Name | Version | Summary | date |
| autotestreg |
0.1.1 |
Automatically test your functions to see if you have changed their behavior by mistake! |
2023-03-27 10:08:25 |
| glmtools |
0.2.1 |
GLMtools |
2023-03-22 23:34:30 |
| fastreg |
1.2 |
Fast sparse regressions |
2023-02-07 18:22:49 |
| pyTsetlinMachine |
0.6.4 |
Implements the Tsetlin Machine, Embedding Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and drop clause/literal. |
2023-02-02 15:03:04 |
| prfr |
0.2.4 |
Probabilitic random forest regression algorithm |
2023-01-25 05:54:07 |
| supervised-ml |
0.0.2 |
machine learning supervised models for python with accuracy metrics |
2023-01-23 04:42:58 |
| DyGyS |
0.0.5 |
DyGyS is a package for Maximum Entropy regression models with gravity specification for undirected and directed network data. Moreover, it can solve them, generate the graph ensemble, compute several network statistics, calculate model selection measures such as AIC and BIC and quantify their reproduction accuracy in topological and weighted properties. |
2023-01-21 10:59:38 |
| woe-monotonic-binning |
0.4.6 |
Optimal binning algorithm and function to apply on a pandas DataFrame |
2023-01-18 20:20:45 |
| 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 |
| symlearn |
0.1.0a0 |
Different Population-based Symoblic Regressors |
2023-01-11 23:03:53 |
| svreg |
0.1.0.post2 |
Module to perform regression using Shapley Values. |
2023-01-04 16:08:19 |
| profit |
0.6 |
Probabilistic response model fitting with interactive tools |
2022-12-23 22:09:11 |
| arboreto |
0.1.6 |
Scalable gene regulatory network inference using tree-based ensemble regressors |
2021-02-09 11:20:21 |