Selecting the best model in scikit-learn using cross-validation

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-learn-videos Documentation on cross-validation: http://scikit-learn.org/stable/modules/cross_validation.html Documentation on model evaluation: http://scikit-learn.org/stable/modules/model_evaluation.html GitHub issue on negative mean squared error: https://github.com/scikit-learn/scikit-learn/issues/2439 An Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ K-fold and leave-one-out cross-validation: https://www.youtube.com/watch?v=nZAM5OXrktY Cross-validation the right and wrong ways: https://www.youtube.com/watch?v=S06JpVoNaA0 Accurately Measuring Model Prediction Error: http://scott.fortmann-roe.com/docs/MeasuringError.html An Introduction to Feature Selection: http://machinelearningmastery.com/an-introduction-to-feature-selection/ Harvard CS109: https://github.com/cs109/content/blob/master/lec_10_cross_val.ipynb Cross-validation pitfalls: http://www.jcheminf.com/content/pdf/1758-2946-6-10.pdf WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/ju