ROC & PR Area Under the Curve (AUC) Metrics скачать в хорошем качестве

ROC & PR Area Under the Curve (AUC) Metrics

In this video we will cover 2 Area Under the Curve (AUC) metrics that are commonly used to evaluate classification models. These include the Receiver Operating Characteristic (ROC) and Precision Recall (PR) AUC metrics. We will discuss what these measures signify, and when it is appropriate to use them. The break-down of this video is as follows: Introduction 00:00 Why AUC metrics? 01:06 Jupyter Notebook ROC AUC example 03:19 Effects of Class Imbalance? 08:27 Jupyter Notebook PR AUC example 12:16 Conclusions 15:15 The best way to keep up-to-date with my video/blog content is to sign up for my monthly Newsletter! Please visit: https://insidelearningmachines.com/ne... to register. The notebook presented here can be found at: https://github.com/insidelearningmach... This video is based off of an article on my blog. You can find that blog article here: https://insidelearningmachines.com/ar... The homepage of my blog is: https://insidelearningmachines.com Other social media includes: Twitter:   / inside_machines   Facebook:   / inside-learning-machines-112215488183517   #machinelearning #datascience #classification #auc #insidelearningmachines