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.
Sundin ang mga tagubiling ito upang patakbuhin ang app na ito:
- 1. Na-download ang application na ito sa iyong PC.
- 2. Ipasok sa aming file manager https://www.onworks.net/myfiles.php?username=XXXXX kasama ang username na gusto mo.
- 3. I-upload ang application na ito sa naturang filemanager.
- 4. Simulan ang OnWorks Linux online o Windows online emulator o MACOS online emulator mula sa website na ito.
- 5. Mula sa OnWorks Linux OS na kasisimula mo pa lang, pumunta sa aming file manager https://www.onworks.net/myfiles.php?username=XXXX gamit ang username na gusto mo.
- 6. I-download ang application, i-install ito at patakbuhin ito.
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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).
Mga tampok
- 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
Wika ng Programming
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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.
