LightAutoML
MLJ.jl
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| LightAutoML | MLJ.jl | |
|---|---|---|
| 1 | 6 | |
| 767 | 1,720 | |
| - | 1.2% | |
| 9.2 | 8.8 | |
| about 2 years ago | 6 days ago | |
| Python | Julia | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
LightAutoML
MLJ.jl
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What is the Julia equivalent of Scikit-Learn?
MLJ.jl is a good Julia ML framework. There's also a Scikitlearn.jl but its more of a wrapper around the sklearn I believe
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My experience working as a technical writer for MLJ
MLJ is a machine learning framework for Julia, which you can kind of infer from the article but it's not super obvious IMO.
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[N] New BetaML v0.8: model definition, hyperparameters tuning and fitting in 2 lines
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, with a detailed tutorial on its usage from Python or R (no wrapper packages are needed) and an extensive interface to MLJ.
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Python vs Julia
You should definitely go with Julia. It has steeper learning curve than python, but it is way more powerful. As for the ecosystem, you shouldn't worry about that much: DataFrames.jl and friends is way better than pandas, MLJ.jl (https://github.com/alan-turing-institute/MLJ.jl) and FastAI.jl(https://github.com/FluxML/FastAI.jl) are great frameworks for regular ML and deepnet. And if at any point you get a feeling that you need some python library, you can always plug it in with PyCall.jl(https://github.com/JuliaPy/PyCall.jl).
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sklearn equivalent for Julia?
Imho, Julia is more diverse in the sense that there is not a single popular ML library. Maybe the Julian equivalent for scikit-learn is MLJ.jl. There is also ScikitLearn.jl, which defines the usual interface of scikit-learn models, and specific algorithms then implement this interface.
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Swift for TensorFlow Shuts Down
Then you haven't looked at Julia's ecosystem.
It may not be quite as mature, but it's getting there quickly.
It's also far more interoperable because of Julia's multiple dispatch and abstract types.
For example, the https://github.com/alan-turing-institute/MLJ.jl ML framework (sklearn on steroids), works with any table object that implements the Tables.jl interface out of the box, not just with dataframes.
That's just one example.
What are some alternatives?
FEDOT - Automated modeling and machine learning framework FEDOT
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
AutoMLPipeline.jl - A package that makes it trivial to create and evaluate machine learning pipeline architectures.
cookiecutter-data-science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
jupyter - Jupyter metapackage for installation, docs and chat
PythonNet - Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.
lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Distributions.jl - A Julia package for probability distributions and associated functions.
Language_Identifier - Language Identification classification using XGBoost
pyTsetlinMachine - Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget