The methods of evaluating the accuracy of a model differ between supervised learning and unsupervised learning.
In a typical linear regression (where continuous values are predicted), there are a couple of ways of measuring the error of the model. Typically, error is measured on the validation and testing datasets, as measuring error on a training dataset (the dataset using which a model is built) is misleading. Hence, error is always measured on the dataset that is not used to build a model.
Get Hands-On Machine Learning on Google Cloud Platform now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
Get Mark Richards’s Software Architecture Patterns ebook to better understand how to design components—and how they should interact.
Dive in for free with a 10-day trial of the O’Reilly learning platform—then explore all the other resources our members count on to build skills and solve problems every day.
Start your free trial Become a member now