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LightGBM download for Windows

Free download LightGBM Windows app to run online win Wine in Ubuntu online, Fedora online or Debian online

This is the Windows app named LightGBM whose latest release can be downloaded as v4.1.0.zip. It can be run online in the free hosting provider OnWorks for workstations.

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

Follow these instructions in order to run this app:

- 1. Downloaded this application in your PC.

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- 3. Upload this application in such filemanager.

- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.

- 5. From the OnWorks Windows 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 and install it.

- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.

Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.

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LightGBM


DESCRIPTION

LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory.

LightGBM supports parallel and GPU learning, and can handle large-scale data. It’s become widely-used for ranking, classification and many other machine learning tasks.



Features

  • Histogram-based algorithms for optimal speed and memory usage
  • Leaf-wise (Best-first) Tree Growth
  • Optimal Split for Categorical Features
  • State-of-art algorithms and parallel learning algorithms
  • GPU Support
  • Supports regression, binary classification, multi-classification and other applications and metrics
  • DART
  • L1/L2 regularization
  • Bagging
  • Column (feature) sub-sample
  • Continued train with input GBDT model
  • Continued train with the input score file
  • Weighted training
  • Validation metric output during training
  • Multiple validation data
  • Multiple metrics
  • Early stopping (both training and prediction)
  • Prediction for leaf index


Programming Language

C++


Categories

Machine Learning

This is an application that can also be fetched from https://sourceforge.net/projects/lightgbm.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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