mljar-supervised
pyGAM
| mljar-supervised | pyGAM | |
|---|---|---|
| 51 | 2 | |
| 2,936 | 839 | |
| 0.6% | - | |
| 8.5 | 2.4 | |
| 17 days ago | 21 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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mljar-supervised
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Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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Fairness in machine learning
It's an Automated Machine Learning python package. It's open-source, you can see how it works on GitHub: https://github.com/mljar/mljar-supervised
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[P] Build data web apps in Jupyter Notebook with Python only
Sure, at the bottom of our website you can subscribe for newsletter.
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
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library / framework to test multiple sklearn regression models at once
If you need a simple and fast solution, go with auto-sklearn Maybe a bit more complex, but very powerful was mljar-supervised
- Python AutoML on Tabular Data with FeatureEng, HP Tuning, Explanations, AutoDoc
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Data Science and full-stack-web development
In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools.
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Learning Python tricks by reading other people's code. But who?
MLJAR AutoML is a Python package for Automated Machine Learning on tabular data with feature engineering, explanations, and automatic documentation.
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'start with a simple model'
I recommend trying my AutoML package. You can easily check many different algorithms. Waht is more, the baseline algorithms are checked (major class predictor for classification and mean predictor for regression). The advance of AutoML is that it is really quick. You dont need to write preprocessing code, just call fit method.
pyGAM
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[P] glum: High performance Python generalized linear modeling, a glmnet alternative!
GLMs are nice and all, but I'd also like to see some love for GAMs. pygam looks pretty much dead for some years now.
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[P] Which Machine Learning Classifiers are best for small datasets? An empirical study
Ah they haven't quite gotten around to supporting multiclass classification yet! https://github.com/dswah/pyGAM/pull/213
What are some alternatives?
optuna - A hyperparameter optimization framework
scikit-learn - scikit-learn: machine learning in Python
autokeras - AutoML library for deep learning
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
glum - High performance Python GLMs with all the features!
PySR - High-Performance Symbolic Regression in Python and Julia
DALEX - moDel Agnostic Language for Exploration and eXplanation
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Empirical_Study_of_Ensemble_Learning_Methods - Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
mljar-examples - Examples how MLJAR can be used
tabmat - Efficient matrix representations for working with tabular data