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MLJAR Studio download for Linux

Free download MLJAR Studio Linux app to run online in Ubuntu online, Fedora online or Debian online

This is the Linux app named MLJAR Studio whose latest release can be downloaded as v1.1.18sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.

Download and run online this app named MLJAR Studio with OnWorks for free.

Follow these instructions in order to run this app:

- 1. Downloaded this application in your PC.

- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 3. Upload this application in such filemanager.

- 4. Start the OnWorks Linux online or Windows online emulator or MACOS online emulator from this website.

- 5. From the OnWorks Linux OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 6. Download the application, install it and run it.

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MLJAR Studio


DESCRIPTION

We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. It is no black box, as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).



Features

  • It uses many algorithms: Baseline, Linear, Random Forest, Extra Trees, LightGBM, Xgboost, CatBoost, Neural Networks, and Nearest Neighbors
  • It can compute Ensemble based on a greedy algorithm from Caruana paper
  • It can stack models to build a level 2 ensemble (available in Compete mode or after setting the stack_models parameter)
  • It can do features preprocessing, like missing values imputation and converting categoricals. What is more, it can also handle target values preprocessing
  • It can do advanced features engineering, like Golden Features, Features Selection, Text and Time Transformations
  • It can tune hyper-parameters with a not-so-random-search algorithm (random-search over a defined set of values) and hill climbing to fine-tune final models
  • It can compute the Baseline for your data so that you will know if you need Machine Learning or not


Programming Language

Python


Categories

Machine Learning

This is an application that can also be fetched from https://sourceforge.net/projects/mljar-studio.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.


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