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In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validation with the train/test split procedure, and we'll also discuss some variations of cross-validation that can result in more accurate estimates of model performance. Download the notebook: https://github.com/justmarkham/scikit... Documentation on cross-validation: http://scikit-learn.org/stable/module... Documentation on model evaluation: http://scikit-learn.org/stable/module... GitHub issue on negative mean squared error: https://github.com/scikit-learn/sciki... An Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ K-fold and leave-one-out cross-validation: • Видео Cross-validation the right and wrong ways: • Видео Accurately Measuring Model Prediction Error: http://scott.fortmann-roe.com/docs/Me... An Introduction to Feature Selection: http://machinelearningmastery.com/an-... Harvard CS109: https://github.com/cs109/content/blob... Cross-validation pitfalls: http://www.jcheminf.com/content/pdf/1... WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: • Machine learning in Python with sciki... 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?su... 3) JOIN "Data School Insiders" to access bonus content: / dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: / justmarkham - Facebook: / datascienceschool - LinkedIn: / justmarkham