AUC ROC Curve, Log Loss, Classification Report in Machine Learning | Classification Metrics
Classification Metrics in Machine Learning Learn essential classification metrics in machine learning and data science, including confusion matrix, F1 score, precision, recall, precision and recall, ROC curve, and AUC. Understand how these metrics help evaluate and improve your models, even for imbalanced datasets. Perfect for beginners, Python developers, and AI enthusiasts! Understanding classification metrics is the key to building reliable and high-performing machine learning models. In this video, we break down the most important evaluation metrics used in Machine Learning — including the AUC-ROC Curve, Log Loss, and the Classification Report. You will learn: What AUC (Area Under Curve) and ROC (Receiver Operating Characteristic) truly represent How Log Loss measures model uncertainty How to interpret Precision, Recall, F1-Score, and Support from the Classification Report Why these metrics are essential for comparing classification algorithms How to use these metrics in real Data Science and Machine Learning projects Whether you're working on binary classification, multi-class models, or preparing for interviews, this tutorial gives you a clear, beginner-friendly yet professional explanation. Perfect for learners, data science enthusiasts, and anyone wanting to evaluate ML models like a pro. --- Github Link: https://github.com/edumentordeepti/Fo... --- Chapter/ Timestamp: 00:00 - intro 00:51 - how to understand balanced accuracy concepts? 06:15 - how to understand roc-auc curve? 14:24 - how to understand log loss & it's use cases? 18:04 - how to relate mcc- matthews correlation coefficient? 19:38 - how to perform cohen's kappa? 22:00 - how to perform classification report? 26:55 - outro --- "Complete Roadmap from Scratch to End AI full course - EduMentor Deepti" Python in AI ➡️ Data Science ➡️ Machine Learning ➡️ Deep Learning ➡️ Generative AI ➡️ Advance Generative AI --- 👉 Do not forget to subscribe and start watching from the beginning to follow the full curriculum. "Learning should be free, accessible, and practical — and that’s exactly what you’ll get here." / @edumentordeepti --- ❓ Any question or queries? Drop them in the comments below! 👍Like, ✅ Subscribe & 👉 🔔 hit bell icon for all notifications, ↗️ Share and 💡Comment for Suggestions ✉️ Email Us – [email protected] #EduMentorDeepti #ClassificationMetrics #MachineLearning #DataScience #Python #ML #AI #DeepLearning #DataAnalysis #ModelEvaluation #AUCCurve #ROCCurve #LogLoss #ClassificationReport #PythonMachineLearning #MLTutorial #LearnMachineLearning #DataScienceTutorial #PythonProgramming #AICommunity #MLCommunity #DataVisualization #TechEducation #PythonForDataScience #PredictiveModeling #BinaryClassification #MultiClassClassification #MLAlgorithms #Analytics #Programming #Coding #TechSkills #DataScienceCommunity --- DISCLAIMER: This video is created for educational purposes only. We do not own any copyrights, all code, resources shared are for learning only and all rights go to their respective owners. Please respect licenses and terms of use when implementing in your projects. The usage is non-commercial and we do not make any profit from it. The sole purpose of this vides is to " Learn & Grow... " together in the field of Artificial Intelligence and Machine Learning..
