| Name | Version | Summary | date |
| mlrl-boomer |
0.10.0 |
A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label classification rules |
2024-05-05 17:02:39 |
| mlrl-seco |
0.10.0 |
A scikit-learn implementation of a separate-and-conquer multi-label rule learning algorithm |
2024-05-05 17:01:59 |
| mlrl-common |
0.10.0 |
Provides common modules to be used by different types of multi-label rule learning algorithms |
2024-05-05 17:01:31 |
| skforecast |
0.12.0 |
Forecasting time series with scikit-learn regressors. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...). |
2024-05-05 12:52:38 |
| mlrl-testbed |
0.10.0 |
Provides utilities for the training and evaluation of multi-label rule learning algorithms |
2024-05-05 00:06:13 |
| PySCIPOpt-ML |
1.1.1 |
automatically formulate and embed ML models into MIPs with SCIP |
2024-05-02 10:04:11 |
| positional-vectorizer |
0.0.7 |
Positional Vectorizer is a scikit-learn transformer that converts text to bag of words vector using a positional ranking algorithm as score |
2024-05-01 23:27:41 |
| sktime |
0.29.0 |
A unified framework for machine learning with time series |
2024-05-01 13:08:27 |
| niaaml-gui |
0.3.0 |
GUI for NiaAML Python package |
2024-04-30 18:39:39 |
| aeon |
0.8.1 |
A toolkit for conducting machine learning tasks with time series data |
2024-04-25 12:14:29 |
| tsml |
0.4.0 |
A toolkit for time series machine learning algorithms. |
2024-04-25 10:29:23 |
| niaaml |
2.0.0 |
Python automated machine learning framework |
2024-04-24 07:17:10 |
| skpro |
2.2.2 |
A unified framework for probability distributions and probabilistic supervised regression |
2024-04-20 19:36:09 |
| haupt |
2.1.8 |
Lineage metadata API, artifacts streams, sandbox, ML-API, and spaces for Polyaxon. |
2024-04-20 12:26:15 |
| scikit-base |
0.7.6 |
Base classes for sklearn-like parametric objects |
2024-04-16 16:39:28 |
| niceml |
0.14.1 |
Welcome to niceML 🍦, a Python-based MLOps framework that uses TensorFlow and Dagster. This framework streamlines the development, and maintenance of machine learning models, providing an end-to-end solution for building efficient and scalable pipelines. |
2024-04-16 12:09:06 |
| scikeras |
0.13.0 |
Scikit-Learn API wrapper for Keras. |
2024-04-11 22:24:07 |
| daal4py |
2024.3.0 |
Intel® oneAPI Data Analytics Library |
2024-04-11 13:21:33 |
| scikit-learn-intelex |
2024.3.0 |
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application. |
2024-04-11 13:21:10 |
| psy-transect |
0.1.0 |
Psyplot plugin for visualizing data along a transect |
2024-04-04 05:13:50 |