xgboost_ray
mljar-supervised
Our great sponsors
- Sonar - Write Clean Python Code. Always.
- Onboard AI - Learn any GitHub repo in 59 seconds
- Revelo Payroll - Free Global Payroll designed for tech teams
- InfluxDB - Collect and Analyze Billions of Data Points in Real Time
| xgboost_ray | mljar-supervised | |
|---|---|---|
| 1 | 51 | |
| 118 | 2,751 | |
| 5.1% | 0.5% | |
| 6.0 | 6.0 | |
| 11 days ago | 7 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
xgboost_ray
We haven't tracked posts mentioning xgboost_ray yet.
Tracking mentions began in Dec 2020.
mljar-supervised
-
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.
-
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
-
[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
-
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
-
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.
-
'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.
-
I'm Looking to Help Contribute, I am very confident with my skills
Automated Machine Learning (AutoML) Python package https://github.com/mljar/mljar-supervised You can check list of open issues. Or I can recommend some just tell me your preferences (Im the main contributor)
-
[D] Bring your own data AI SaaS service for non-programmers?
Instead, we started to work on desktop application that will allow to create python notebooks with no-code GUI (https://github.com/mljar/studio some screenshots on our website ).
What are some alternatives?
optuna - A hyperparameter optimization framework
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.
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
PySR - High-Performance Symbolic Regression in Python and Julia
studio - MLJAR Studio Desktop Application
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
mljar-examples - Examples how MLJAR can be used
automlbenchmark - OpenML AutoML Benchmarking Framework
lleaves - Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.
OpenBBTerminal - Investment Research for Everyone, Everywhere.
mercury - Convert Jupyter Notebooks to Web Apps