Multi Select

A Python package for integrating scikit-learn feature selection with multi-label problem transformation methods.

Django admin filter for multiple select

A robust and flexible Python package designed for selecting the most discriminatory features in both **binary and multi-class classification problems** using the Kolmogorov-Smirnov (K-S) test. It provides advanced options for handling multi-class scenarios and aggregating p-values.

Allows setting multiple VAT numbers on any partner and select the right one depending on the fiscal position and delivery address of the invoice.

Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python

Select individually the partner visibility on each company

Select individually the product template visibility on each company

A multi-select button group

A custom PyQt6 widget providing a multi-select combobox functionality for PyQt6 applications.

DeepCoMP: Self-Learning Dynamic Multi-Cell Selection for Coordinated Multipoint (CoMP)

Select individually the product template visibility on each company

Multi-model Feature Importance Scoring and Auto Feature Selection

A multi-select button group

Select individually the partner visibility on each company

MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems

MSDA - An open source, low-code time-series multi-sensor data analysis, unsupervised feature selection, deep unsupervised anomaly detection & explainable time-series predictor library in Python.

Tags multiple selection

Streamlit component that allows you to render a menu widget with multiple columns but with only one globally selected value. It is possible to use photos for background inside the menu buttons.

Feature Selection Module for Data Sciences in Python

Automatic Deep Learning, towards fully automated multi-label classification for image, video, text, speech, tabular data.

Select individually the product template visibility on each company

Allows setting multiple VAT numbers on any partner and select the right one depending on the fiscal position and delivery address of the invoice.

Select individually the partner visibility on each company

Tags multiple selection

Select individually the partner visibility on each company

UniLVQ: A Unified Learning Vector Quantization Framework for Supervised Learning Tasks

Select individually the product template visibility on each company

Select individually the partner visibility on each company

Multi-objective evolutionary feature selection.

Select individually the product template visibility on each company

Streamlit component that allows you to choose multi selection but not extend your widget

Tags multiple selection

Multi armed bandit feature selection

This python package simplifies the manipulation with the lists of multi-dimensional dictionaries as select, filter or join.

Select individually the partner visibility on each company

Select individually the product template visibility on each company

Select individually the partner visibility on each company

Tags multiple selection

Select individually the product visibility on each company

Select individually the partner visibility on each company

Select individually the product visibility on each company

Allows setting multiple VAT numbers on any partner and select the right one depending on the fiscal position and delivery address of the invoice.

Tags multiple selection

One-class Classifier Dynamic Ensemble Selection for Multi-class problems

Using Kazoo to implement specific features of ZooKeeper, such as: program survival monitoring (alive), global configuration, single-point resource lock, and multi-selection resource lock.

A module for selecting an item from a list of multi-field data in terminal

This module allows a shortcut to add purchase.order.line by selecting product into a wizard for the given suplier

multi select dropdown

A toolkit for use with pytorch. Self-contained imperative programming, highly customizable, comes with its own instruction controller oriented to complex processes (partly multithreaded parallel, partly sequential serial, and able to be nested). Has built-in features to instantly visualize the training process (highly customizable), generate logs, auto-save, pause and resume training, automatic device selection, etc., and thanks to the use of multi-threading, it has no impact on the training speed at all.

Package with basic implementations of mono and multi-objective genetic algorithms for feature selection.