Course Content

  • 4_7_Evaluating_Classification_Models_Performance

Course Content

FAQs

  • How to Evaluate a Classification Model Properly
  • Classification precision.
  • Matrix of perplexity
  • Precision and memory.
  • F1 rating.
  • Specificity and sensitivity
  • AUC and ROC curve

Confusion Matrix for Classification Model Evaluation A confusion matrix is a n x n matrix used to describe the performance of a classification model (where n is the number of labels). In the confusion matrix, each row represents an actual class, while each column represents a predicted class.

To accomplish this, use the model to predict the answer using the evaluation dataset (held out data), and then compare the expected target to the actual answer (ground truth). A variety of measures are used in machine learning to assess a model's predicted performance. The accuracy metric used is determined by the ML task.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere