How to Test Machine Learning Models | Avoid Data Leakage in Computer Vision | Ultralytics YOLO11🚀 скачать в хорошем качестве

How to Test Machine Learning Models | Avoid Data Leakage in Computer Vision | Ultralytics YOLO11🚀

In this tutorial, we will explore how to test the machine learning models. You'll gain a comprehensive understanding of preparing models for testing, distinguishing between validation and testing, and avoiding common pitfalls such as overfitting and data leakage. Discover valuable insights into ensuring robust machine learning performance. Key Highlights: 00:00 - Introduction to Model Testing: Overview of the model testing process and its significance in machine learning workflows. 00:15 - How to Evaluate the Machine Learning Model after Training: Overview of evaluation methods for Ultralytics YOLO11 Models. 00:48 - Difference between Model Validation and Model Testing: Understanding the roles of validation and testing in model development. 01:36 - Preparation for Model Testing and How to Test the Models 04:05 - Overfitting and Underfitting in Machine Learning: Insights into detecting, addressing, and balancing these critical issues. 05:00 - Data Leakage in Computer Vision and How to Avoid It: Strategies to prevent data leakage and ensure fair evaluation results. 05:58 - Conclusion and Summary: Key takeaways, practical tips, and next steps for deploying models. Learn more ➡️ https://docs.ultralytics.com/guides/m... 🔗 Key Ultralytics Resources: 🏢 About Us: https://ultralytics.com/about 💼 Join Our Team: https://ultralytics.com/work 📞 Contact Us: https://ultralytics.com/contact 💬 Discord Community:   / discord   📄 Ultralytics License: https://ultralytics.com/license 🔬 Ultralytics YOLO Resources: 💻 GitHub Repository: https://github.com/ultralytics/ 📚 Documentation: https://docs.ultralytics.com/ Stay updated with our latest innovations in AI and computer vision. Subscribe to our channel for tutorials, product updates, and insights from industry experts! #Ultralytics #YOLO #ComputerVision #AI #MachineLearning #DeepLearning