MLBox VS nylon

Compare MLBox vs nylon and see what are their differences.

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MLBox nylon
1 5
1,475 84
- -
0.0 0.0
9 months ago almost 3 years ago
Python Python
GNU General Public License v3.0 or later 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.

MLBox

Posts with mentions or reviews of MLBox. We have used some of these posts to build our list of alternatives and similar projects.

nylon

Posts with mentions or reviews of nylon. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing MLBox and nylon you can also consider the following projects:

lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

Gramformer - A framework for detecting, highlighting and correcting grammatical errors on natural language text. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

AlphaPy - Python AutoML for Trading Systems and Sports Betting

deep-fast-vision - A Python library for rapid prototyping of deep transfer learning vision models.

draf - Demand Response Analysis Framework (DRAF)

orion - Asynchronous Distributed Hyperparameter Optimization.

gmr - Gaussian Mixture Regression

aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

ML.NET-Auto-ML-ESRB-Rating-Classification - Classifying Game ESRB Ratings using ML.NET Auto ML

deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Keras - Deep Learning for humans