semvec/pypi-packages huggingface dataset:
evoml.framework 1.0.3
An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python
License: MIT
Keywords: iot

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<img alt="EvoCC-logo" src="https://raw.githubusercontent.com/Evo-ML/Evo-CC/main/evocc-logo.png" width=40%>
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# EVO-CC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python

**Description**: The framework is an open source and cross-platform framework implemented in Python which uses the classification technique with the evolutionary clustering approach provided by the EvoCluster framework. The goal of this framework is to provide a user-friendly and customizable implementation of the classification-based evolutionary clustering which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the framework for their research studies.

**Framework folders and files:**
1.  EvoCluster folder 
2.  Dataset folder 
3.  Classification file
4.  Examples files
ForwardStepwiseFeatureSelection 1.3.9
Performaning feature selection in forward stepwise selection maner.
License: MIT
Requires Python: >=3.6
Keywords: forward stepwise features feature select selection machine learning data science computer science recursive feature elimination dimentionality reduction

# <img src="https://raw.githubusercontent.com/HindyDS/ForwardStepwiseFeatureSelection/main/logo/RFS%2010.5.2021.png" height="277">

[![Open Source Love](https://badges.frapsoft.com/os/v2/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/)
[![PyPI version](https://badge.fury.io/py/RecursiveFeatureSelector.svg)](https://badge.fury.io/py/RecursiveFeatureSelector)
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ForwardStepwiseFeatureSelection aims to select the best features or the subset of features in machine learning tasks according to corresponding score with other incredible packages like numpy, pandas and sklearn.

This package is inspired by: 
PyData DC 2016 | A Practical Guide to Dimensionality Reduction 
Vishal Patel
October 8, 2016

- **Examples:** https://github.com/HindyDS/ForwardStepwiseFeatureSelection/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/ForwardStepwiseFeatureSelection/tree/main/ForwardStepwiseFeatureSelection
- **Bug reports:** https://github.com/HindyDS/ForwardStepwiseFeatureSelection/issues

It requires at least six arguments to run:

- estimators: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html

Optional arguments:
- max_trial (int): number of trials that you wanted RFS to stop searching
- tolerance (int): how many times RFS can fail to find better subset of features 
- least_gain (int): threshold of scoring metrics gain in fraction 
- max_feats (int): maximum number of features
- prior (list): starting point for RFS to search, must be corresponds to the columns of X
- exclusions (nested list): if the new selected feature is in one of the particular subpool 
		    (list in the nested list), then the features in that particular subpool with no 			    longer be avalible to form any new subset in the following trials
- n_jobs (int): Number of jobs to run in parallel.
- n_digit (int): Decimal places for scoring
- verbose (int): Level of verbosity of RFS

If you have any ideas for this packge please don't hesitate to bring forward!
test-pypi-name 0.0.0
Algorithmic train:test splitting for molecules, images, and arbitrary arrays.
License: MIT

# extended_train_test_split
## Using this template:
1. Run `start_project.py`, which will gather some info and rename everything.
2. Add tests.
3. Add your source code to your source directory.
4. Add your requirements to `./requirements.txt`.
5. Curate examples as python notebooks and add them to `./examples/`.
6. Replace `./extended_train_test_split_logo.png` with your logo.
7. Delete this line and everything above it, as well as `start_project.py`.

<h1 align="center">BlankPythonProject</h1> 
<h3 align="center">Algorithmic train:test splitting for molecules, images, and arbitrary arrays.</h3>

<p align="center">  
  <img alt="extended_train_test_splitlogo" src="https://github.com/JacksonBurns/extended_train_test_split/blob/main/extended_train_test_split_logo.png">
</p> 
<p align="center">
  <img alt="GitHub Repo Stars" src="https://img.shields.io/github/stars/JacksonBurns/extended_train_test_split?style=social">
  <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/extended_train_test_split">
  <img alt="PyPI" src="https://img.shields.io/pypi/v/extended_train_test_split">
  <img alt="PyPI - License" src="https://img.shields.io/github/license/JacksonBurns/extended_train_test_split">
</p>

## Online Documentation
[Click here to read the docuemtntation](https://JacksonBurns.github.io/extended_train_test_split/)