Machine Learning EXPERTS Expose Top Cross Validation(CV) Mistakes

Cross-validation is a core concept in machine learning and data science, used to evaluate model performance using reliable cross-validation techniques. Methods like k-fold cross-validation, stratified k-fold cross-validation, leave-one-out cross-validation, and leave-p-out cross-validation represent the main types of cross-validation applied in practice. Key Concepts Included: Train/Test Split vs Cross-Validation – why single split is risky. K-Fold Cross-Validation – data split into k equal folds; each fold becomes a test set once. Stratified K-Fold – maintains class balance; best for classification. Repeated K-Fold – repeats K-Fold multiple times for more stable metrics. Leave-One-Out Cross-Validation (LOOCV) – uses 1 sample as test, rest for training; useful but computationally heavy. Leave-P-Out – extension of LOOCV testing on ‘p’ observations. Time Series Cross-Validation – uses forward chaining and respects temporal order; crucial for forecasting. Nested Cross-Validation – handles model selection + performance estimation without data leakage. Why CV Works – reduces variance, improves generalization, and provides robust model comparison. Where It’s Used – regression, classification, hyperparameter tuning, model selection, avoiding overfitting. This guide helps learners understand when, where, and how to use cross-validation across different models—linear models, tree-based models, ensemble techniques, and even deep learning pipelines. --- Github Link: https://github.com/edumentordeepti/For-Youtubers --- Chapter/ Timestamp: 00:00 - intro 00:38 - what is cross validation concept & how it works? 04:09 - how to understand different types of cross validation? 05:07 - how to understand steps of hold-out cross validation? 06:06 - how to understand steps of k-fold cross validation? 12:04 - how to understand steps of leave-one-out cross validation? 15:15 - how to understand steps of leave-p-out cross-validation? 16:36 - how to understand steps of stratified k-fold cross-validation? 19:57 - how to understand steps of repeated k-fold cross-validation? 24:54 - how to understand steps of nested k-fold cross validation? 27:15 - how to determine time series cross validation? 29:45 - how to choose particular cross-validation? 35:13 - 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." https://www.youtube.com/@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] --- #MachineLearning #DataScience #CrossValidation #MLTips #AI #DeepLearning #Python #Coding #DataScientist #MLAlgorithms #ModelEvaluation #BigData #TechEducation #LearnMachineLearning #Programming #AICommunity #TechTok #StudyShorts #MachineLearningShorts #YouTubeLearning #DataScienceShorts #EduShorts #Analytics #MLEngineer #AIRevolution #CodingLife #TechContent #ViralLearning #TrendingTech #EduMentorDeepti#DailyLearning #TrendingNow #Trending #Viral #CreatorCommunity #Learning #Education #Tech #Inspiration #Career #Success #Skills #HowTo #Tutorial #Productivity --- 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..