How to Interpret Bayesian Ridge Regression with Scikit-Learn

Complete tutorial on 'How to make predictions with Scikit-Learn' can be found here: https://www.activestate.com/resources/quick-reads/how-to-make-predictions-with-scikit-learn/ This video is only a part of a larger tutorial. In this example, the BayesianRidge estimator class is used to predict new values in a regression model that lacks sufficient data. A linear regression is formulated using a probable distribution of values in the absence of actual values. The output, response 'y', is derived from the probable distribution rather than from actual values. If you want to learn more about linear regression, please follow this link: https://www.activestate.com/resources/quick-reads/how-to-make-predictions-with-scikit-learn/ Get a version of Python, pre-compiled with Scikit-learn, NumPy, pandas and other popular ML Packages. Check out ActivePython for ML - https://www.activestate.com/products/python/ View all our Python tutorials related to popular Python packages here: https://www.activestate.com/resources/quick-reads/

Видео How to Interpret Bayesian Ridge Regression with Scikit-Learn автора Боты: от идеи до реализации