Скачать с ютуб Comparing machine learning models in scikit-learn в хорошем качестве

Comparing machine learning models in scikit-learn

We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform on new data. We'll use that same process to locate optimal tuning parameters for a KNN model, and then we'll re-train our model so that it's ready to make real predictions. Download the notebook: https://github.com/justmarkham/scikit... Quora explanation of overfitting: http://www.quora.com/What-is-an-intui... Estimating prediction error:    • Видео   Understanding the Bias-Variance Tradeoff: http://scott.fortmann-roe.com/docs/Bi... Guiding questions for that article: https://github.com/justmarkham/DAT8/b... Visualizing bias and variance: http://work.caltech.edu/library/081.html 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