MLJAR Studio download for Linux

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.

Suivez ces instructions pour exécuter cette application :

- 1. Téléchargé cette application sur votre PC.

- 2. Entrez dans notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous voulez.

- 3. Téléchargez cette application dans ce gestionnaire de fichiers.

- 4. Démarrez l'émulateur en ligne OnWorks Linux ou Windows en ligne ou l'émulateur en ligne MACOS à partir de ce site Web.

- 5. Depuis le système d'exploitation OnWorks Linux que vous venez de démarrer, accédez à notre gestionnaire de fichiers https://www.onworks.net/myfiles.php?username=XXXXX avec le nom d'utilisateur que vous souhaitez.

- 6. Téléchargez l'application, installez-la et exécutez-la.

CAPTURES D'ÉCRAN:


Studio MLJAR


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


Langage de programmation

Python


Catégories

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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