| Name | Version | Summary | date |
| pyoselm |
1.2.0 |
A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning |
2025-08-17 20:58:19 |
| scikit-plots |
0.4.0.post3 |
An intuitive library that seamlessly adds plotting capabilities and functionality to any model objects or outputs, compatible with tools like scikit-learn, XGBoost, TensorFlow, and more. |
2025-08-17 19:15:19 |
| skpro |
2.9.3 |
A unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in python |
2025-08-17 18:30:14 |
| scikit-base |
0.12.5 |
Base classes for sklearn-like parametric objects |
2025-08-17 16:13:19 |
| haupt |
2.9.3 |
Lineage metadata API, artifacts streams, sandbox, ML-API, and spaces for Polyaxon. |
2025-08-15 15:28:33 |
| pytabkit |
1.6.1 |
ML models + benchmark for tabular data classification and regression |
2025-08-14 10:49:22 |
| skforecast |
0.17.0 |
Skforecast is a Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others. |
2025-08-11 14:48:59 |
| xrfm |
0.2.0 |
xRFM: Scalable and interpretable kernel methods for tabular data |
2025-08-10 16:51:01 |
| mlrl-testbed-sklearn |
0.12.3 |
Adds support for the scikit-learn framework to the package "mlrl-testbed" |
2025-08-02 16:56:40 |
| mlrl-seco |
0.12.3 |
A scikit-learn implementation of a Separate-and-Conquer (SeCo) multi-label rule learning algorithm |
2025-08-02 16:56:23 |
| mlrl-common |
0.12.3 |
Provides common modules to be used by different types of multi-label rule learning algorithms |
2025-08-02 16:56:04 |
| mlrl-boomer |
0.12.3 |
A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label output rules |
2025-08-02 16:55:44 |
| NiaAML-GUI |
0.4.2 |
GUI for NiaAML Python package |
2025-07-31 08:34:56 |
| decisioncanvas |
1.0.1 |
Easy decision boundary visualization for classifiers |
2025-07-27 07:00:04 |
| skfolio |
0.11.0 |
Portfolio optimization built on top of scikit-learn |
2025-07-26 20:36:29 |
| mlfcrafter |
0.1.1 |
ML Pipeline Automation Framework - Chain together data processing, model training, and deployment with minimal code |
2025-07-26 10:48:34 |
| nlpbasekit |
0.1.4 |
An easy-to-use library with advanced preprocessing features to streamline and accelerate machine learning workflows. |
2025-07-25 14:26:05 |
| automl-framework |
0.1.1 |
A comprehensive, modular framework for automated machine learning with overfitting detection and mitigation |
2025-07-23 02:27:05 |
| MaldiAMRKit |
0.2.2 |
Toolkit to read and preprocess MALDI-TOF mass-spectra for AMR analyses. |
2025-07-22 12:44:40 |
| automl-lite |
0.1.1 |
A simplified automated machine learning package for non-experts |
2025-07-22 07:43:56 |