OPENCV & C++ TUTORIALS - 201 | Machine Learning | Boost Parameters скачать в хорошем качестве

OPENCV & C++ TUTORIALS - 201 | Machine Learning | Boost Parameters

🚀 Welcome to Tutorial 201 in our OpenCV & C++ Series! In this video, we explore Boosted Trees, one of the most powerful and widely-used ensemble machine learning algorithms. 🌳⚡ Learn how to train and predict using cv::ml::Boost in C++, and understand how combining multiple weak learners (decision trees) can create a strong classifier. We’ll break down key concepts like weak classifiers, boosting iterations, tree depth, and how Boost adapts to misclassified samples to improve accuracy. You’ll also see practical coding examples with linear and non-linear datasets, decision boundary visualization, and tips for tuning parameters like the number of trees, tree depth, and learning type. Whether it’s object classification, feature-based prediction, or any computer vision ML task, Boosted Trees are a versatile tool in your toolkit. 🔍📊 🎯 What you’ll learn: How Boosted Trees work and why boosting improves accuracy Training & prediction with cv::ml::Boost in OpenCV C++ Understanding weak learners, tree depth, and boosting iterations Visualizing decision boundaries for linear and circular data Comparing Boost with other classifiers like SVM, Logistic Regression & Bayes Step-by-step code walkthrough with practical examples 📚 Perfect for: Developers learning OpenCV ML, C++ beginners exploring ensemble methods, and anyone working on classification tasks for computer vision projects! 💻 Code + Theory + Demos = Complete Understanding 📌 Missed Tutorial 199? Check it out to learn Logistic Regression with OpenCV & C++! 🌠 Boost class: https://docs.opencv.org/4.8.0/d6/d7a/... 🌠 Tutorial Series Plan: https://docs.opencv.org/4.8.0/modules... 🌠 Stackoverflow: https://stackoverflow.com/users/11048... 🌠 Github: https://github.com/yunus-temurlenk?ta... 🌠 Twitter:   / code_enjoy   🌠 Hashnode: https://yunustemurlenk.hashnode.dev/ ▬ Contents of this video ▬▬▬▬▬▬▬▬▬▬ 0:00 - Introduction 2:40 - Coding & Explanation If you see any mistake or have advice, please comment. Thanks for watching… #opencv #tutorial #Boost #MachineLearning