| Name | Version | Summary | date |
| hypertune |
1.1.0 |
A library for performing hyperparameter optimization with Polyaxon. |
2023-11-02 08:58:15 |
| pmdarima |
2.0.4 |
Python's forecast::auto.arima equivalent |
2023-10-23 13:55:11 |
| scikit-datasets |
0.2.4 |
Scikit-learn-compatible datasets |
2023-08-08 14:02:18 |
| sklearn-nature-inspired-algorithms |
0.12.0 |
Search using nature inspired algorithms over specified parameter values for an sklearn estimator. |
2023-08-05 18:50:42 |
| pyaf |
5.0 |
Python Automatic Forecasting |
2023-07-12 22:24:01 |
| sktree |
0.1.3 |
Modern decision trees in Python |
2023-07-05 23:35:30 |
| mlrl-common |
0.9.0 |
Provides common modules to be used by different types of multi-label rule learning algorithms |
2023-07-02 17:37:25 |
| mlrl-boomer |
0.9.0 |
A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label classification rules |
2023-07-02 17:37:20 |
| mlrl-testbed |
0.9.0 |
Provides utilities for the training and evaluation of multi-label rule learning algorithms |
2023-07-02 16:27:44 |
| lazyforecast |
0.0.1 |
LazyForecast is a Python library for performing univariate time series analysis using a lazy forecasting approach. This approach is designed to provide quick and simple forecasting models without requiring extensive configuration or parameter tuning. |
2023-06-27 10:10:26 |
| skbase |
0.4.6 |
Base classes for sklearn-like parametric objects |
2023-06-17 20:17:58 |
| example-wise-f1-maximizer |
0.1.5 |
A scikit-learn meta-estimator for multi-label classification that aims to maximize the example-wise F1 measure |
2023-06-16 11:15:57 |
| scikit-MDR |
0.4.5 |
Multifactor Dimensionality Reduction (MDR) |
2023-06-14 17:45:02 |
| DataDoctor |
1.0.15 |
A Python package for data cleaning and preprocessing. |
2023-06-03 20:19:28 |
| nlpstack |
0.1.0 |
Modules for NLP |
2023-05-07 11:57:05 |
| skdoc |
0.0.1.5 |
Automated documentation engine for scikit-learn models |
2023-04-16 16:16:19 |
| rapidpredict |
0.0.0.9 |
rapid predict is a python package to simplifies the process of fitting and evaluating multiple machine learning models on a dataset. |
2023-04-14 03:43:24 |
| quality-assurance-data |
0.0.0.1 |
Quality Assurance data and machine learning |
2023-04-10 13:50:01 |
| synthetic-dataset |
0.0.0.2 |
Generating accurate and safe synthetic datasets for tabular, classification, and time-series labeling tasks |
2023-04-10 04:37:01 |
| patatas |
0.1.1 |
A powerful package for K-NN regression, data preprocessing, and analysis for Data Science |
2023-03-27 10:09:54 |