MLJ.jl
pyTsetlinMachine
| MLJ.jl | pyTsetlinMachine | |
|---|---|---|
| 6 | 1 | |
| 1,722 | 122 | |
| 0.6% | 0.8% | |
| 8.7 | 5.8 | |
| 8 days ago | about 2 months ago | |
| Julia | C | |
| GNU General Public License v3.0 or later | MIT License |
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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.
pyTsetlinMachine
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[R] Drop Clause boosts Tsetlin Machine accuracy up to +4% and training speed up to 4x
Hi u/pddpro, there are unfortunately few tutorials available. There are some video resources here: https://github.com/cair/TsetlinMachine#videos and some demos here: https://github.com/cair/pyTsetlinMachine. Currently writing a book: Introduction to Tsetlin Machines. Plan to share drafts of the chapters here as I proceed, including tutorials.
What are some alternatives?
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
TsetlinMachine - Code and datasets for the Tsetlin Machine
AutoMLPipeline.jl - A package that makes it trivial to create and evaluate machine learning pipeline architectures.
Tribuo - Tribuo - A Java machine learning library
Enzyme.jl - Julia bindings for the Enzyme automatic differentiator
rpart - Recursive Partitioning and Regression Trees
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.
pycaret - An open-source, low-code machine learning library in Python
Distributions.jl - A Julia package for probability distributions and associated functions.
SLID-on-Microcontrollers - Speech Classification using a Convolutional Neural Network running on a Microcontroller
Tumble.jl - lazy predictive modeling for julia
ARCHModels.jl - A Julia package for estimating ARMA-GARCH models.