Sklearn-genetic-opt VS sklearn-deap

Compare Sklearn-genetic-opt vs sklearn-deap and see what are their differences.

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Sklearn-genetic-opt sklearn-deap
6 1
271 758
- -
4.6 0.0
6 days ago 3 months ago
Python Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Sklearn-genetic-opt

Posts with mentions or reviews of Sklearn-genetic-opt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-02.

sklearn-deap

Posts with mentions or reviews of sklearn-deap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-02.

What are some alternatives?

When comparing Sklearn-genetic-opt and sklearn-deap you can also consider the following projects:

genetic-algorithm-in-python - A genetic algorithm written in Python for educational purposes.

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

evalml - EvalML is an AutoML library written in python.

de-torch - Minimal PyTorch Library for Differential Evolution

Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

FEDOT - Automated modeling and machine learning framework FEDOT

zoofs - 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 easy to use , flexible and powerful tool to reduce your feature size.

MachineLearningStocks - Using python and scikit-learn to make stock predictions

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

powershap - A power-full Shapley feature selection method.

nyc_traffic_flask - Flask App with leaflet.js that can perform NYC Traffic Prediction