Course Content

  • Evaluating_Classification_Models_Performance

Course Content


The performance of a classification model where the prediction is a probability value between 0 and 1 is measured by logarithmic loss (or log loss). As the anticipated probability diverges from the actual label, log loss grows. For Kaggle contests, log loss is a popular measure.

The evaluation of performance is an important part of the machine learning process. It is, however, a difficult task. As a result, it must be carried out with caution if machine learning can be applied to radiation oncology or other fields with confidence.

Model assessment is the process of analysing a machine learning model's performance, as well as its strengths and weaknesses, using various evaluation criteria. Model evaluation is critical for determining a model's efficacy during the early stages of research, as well as for model monitoring.

The main types of evaluation are processes:

  • impact
  • outcome
  • summative
  • evaluation.

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