DEV Community

The Nerdy Dev
The Nerdy Dev

Posted on

Precision, Recall, Confusion Matrix and F1-Score | Performance Evaluation Metrics for Classification

In this video, we will learn about the performance evaluation metrics for classification models namely accuracy, confusion matrix and the ROC-AUC Curve (Receiver Operating Characteristic. We will first understand each of these metrics in detail:

  1. What is Precision in Machine Learning ?
  2. What is Accuracy in Machine Learning ?
  3. How to compute Precision and Recall to evaluate the performance for our classifiers ?
  4. How to read the confusion matrix ?
  5. How to draw a confusion matrix ?
  6. Interpreting the confusion matrix that is given to us.
  7. What does the confusion matrix gives?
  8. What is ROC-AUC Curve and how it is used to distinguish the performance of classifiers ?
  9. How to use ROC-AUC Curve to determine which classifier is the best classifier and which classifier is the worst one ? and more...

๐Ÿฑโ€๐Ÿ’ป ๐Ÿฑโ€๐Ÿ’ป Course Links:
Complete Code - https://github.com/The-Nerdy-Dev
Visual Studio Code - https://code.visualstudio.com
Git - https://git-scm.com/downloads


Support my channel:
๐Ÿ’œ Join the Discord community ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘งโ€๐Ÿ‘ฆ: https://discord.gg/fgbtN2a
๐Ÿ’œ One time donations via PayPal
Thank you! ๐Ÿ™


Follow me on:
๐Ÿ‘‰ Twitter: https://twitter.com/The_Nerdy_Dev
๐Ÿ‘‰ Instagram: https://instagram.com/thenerdydev
๐Ÿ‘‰ My Blog: https://the-nerdy-dev.com

Top comments (0)