Auto_ViML
mljar-supervised
| Auto_ViML | mljar-supervised | |
|---|---|---|
| 2 | 51 | |
| 490 | 2,936 | |
| - | 0.8% | |
| 2.2 | 8.5 | |
| 7 days ago | 21 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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Auto_ViML
- library / framework to test multiple sklearn regression models at once
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AutoNLP for Automating Twitter Sentiment Analysis
To install this we can use a simple pip command. Since AutoNLP belongs to autoviml we need to install that.
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
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- 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.
What are some alternatives?
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
optuna - A hyperparameter optimization framework
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
autokeras - AutoML library for deep learning
Python-Schema-Matching - A python tool using XGboost and sentence-transformers to perform schema matching task on tables.
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.
evalml - EvalML is an AutoML library written in python.
PySR - High-Performance Symbolic Regression in Python and Julia
sapientml - Generative AutoML for Tabular Data
Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
mljar-examples - Examples how MLJAR can be used