Course Content

Course Content

FAQs

By examining the prediction error on the training and evaluation data, we may assess whether a predictive model is underfitting or overfitting the training data. When your model performs poorly on the training data, it is underfitting the training data.

Overfitting is a modeling error that arises when a function is overly closely fitted to a small number of data points. Underfitting is defined as a model that cannot both model the training data and generalize to new data.

The model gets "overfitted" when it memorizes the noise and fits too closely to the training set, and it is unable to generalize adequately to new data. If a model is unable to generalize adequately to new data, it will be unable to accomplish the classification or prediction tasks for which it was designed.

Variance relates to how the model evolves when different parts of the training data set are used. Simply put, variance is the variability in model prediction—how much the ML function can change based on the data set. Variance comes from highly complex models with a large number of features.

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