A simple R wrapper for mljar.com API. It allows MLJAR users to create Machine Learning models with few lines of code:
library(mljar) model <- mljar_fit(x.training, y.training, validx=x.validation, validy=y.validation, proj_title="Project title", exp_title="experiment title", algorithms = c("logreg"), metric = "logloss") predicted_values <- mljar_predict(model, x.to.predict, "Project title")
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 directly from CRAN:
Alternatively, you can install the latest development version from GitHub using
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 in shell:
or directly in RStudio:
- That's all, you are ready to use MLJAR in your R 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 function
mljar_fityou 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
mljar_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
To run tests use simple command in your R session: