Скачать с ютуб Selecting the best model in scikit-learn using cross-validation в хорошем качестве

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... 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