A simple python wrapper over mljar API. It allows MLJAR users to create Machine Learning models with few lines of code:
from mljar import Mljar model = Mljar(project='My awesome project', experiment='First experiment') model.fit(X,y) model.predict(X)
That's all folks! Yeah, I know, this makes Machine Learning super easy! You can use this code for following Machine Learning tasks:
- Binary classification (your target has only two unique values)
- Regression (your target value is continuous)
- More is coming soon!
How to install
You can install mljar with pip:
pip install -U mljar
or from source code:
python setup.py install
How to use it
- Create an account at mljar.com and login.
- Please go to your users settings (top, right corner).
- Get your token, for example 'exampleexampleexample'.
- Set environment variable
MLJAR_TOKENwith your token value:
- That's all, you are ready to use MLJAR in your python code!
What's going on?
- This wrapper allows you to search through different Machine Learning algorithms and tune each of the algorithm.
- By searching and tuning ML algorithm to your data you will get very accurate model.
- By calling method
Mljar classyou create new project and start experiment with models training. All your results will be accessible from your mljar.com account - this makes Machine Learning super easy and keeps all your models and results in beautiful order. So, you will never miss anything.
- All computations are done in MLJAR Cloud, they are executed in parallel. So after calling
fitmethod you can switch your computer off and MLJAR will do the job for you!
- I think this is really amazing! What do you think? Please let us know at
The examples are here!.
To run tests with command:
python -m tests.run