Implementing a Regularization Pipeline in Python - Meaningful Predictive Modeling скачать в хорошем качестве

Implementing a Regularization Pipeline in Python - Meaningful Predictive Modeling

Link to this course: https://click.linksynergy.com/deeplin... Implementing a Regularization Pipeline in Python - Meaningful Predictive Modeling Python Data Products for Predictive Analytics Specialization This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is good enough? Or how do we decide which of two regression algorithms is better? By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or unseen) data. Implementing a Regularization Pipeline in Python - Meaningful Predictive Modeling Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.