A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

Code repository for the online course Feature Selection for Machine Learning

Examples for https://github.com/optuna/optuna

Plugin para ejecutar el modelo de META Segment Anything en QGIS

A Python package for interactive mapping and geospatial analysis with minimal coding in a Jupyter environment

Canonical Correlation Forests (CCFs) in python

source code for google earth engine mapping routines

A complete alternative for Overleaf with VSCode + Web + Git Integration + Grammarly + Live Collaboration Support

Principles of Machine Learning Python

The largest library developed with complete focus on Feature Selection (FS) using Meta-Heuristic Algorithm / Nature-inspired evolutionary / Swarm-based computing.

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

Implementation of the Simard et al., SRTM based estimation of mangrove height and above ground biomass

Python automated machine learning framework.

ACOLITE: generic atmospheric correction module

Python microframework for building nature-inspired algorithms. Official docs: https://niapy.org

Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm

🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.

zoofs is a Python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It's…

A Python Package for Feature Selection

A Python implementation of global optimization with gaussian processes.

A collection of the state-of-the-art MEta-heuristics ALgorithms in PYthon (mealpy)

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

QGIS 3.x Plugin to generate SPI values from daily precipitation raster.

Heuristic global optimization algorithms in Python

A hyperparameter optimization and meta-learning toolbox for convenient and fast prototyping of machine-/deep-learning models.

This toolbox offers 13 wrapper feature selection methods (PSO, GA, GWO, HHO, BA, WOA, and etc.) with examples. It is simple and easy to implement.