This video is about Unit #7 Lesson 5: Introduction to smoothing splines
Пікірлер: 12
@gurns6812 жыл бұрын
Great simple explanations. Thank you
@25lobo4 жыл бұрын
Excellent explanation.
@ishwarshrestha29653 жыл бұрын
This is great! Thank you a lot
@user-ix1yl1ue8u Жыл бұрын
This was very helpfull, thanks a lot!
@pranavvinod168410 ай бұрын
🎯 Key Takeaways for quick navigation: 00:00 📊 Smoothing splines are a non-parametric regression method used to model relationships between variables without assuming a specific functional form. 01:35 📉 Smoothing splines help strike a balance between overfitting (interpolation) and underfitting (oversimplification) by penalizing functions for being too wiggly or rough. 03:28 🧐 The smoothing parameter (lambda) in smoothing splines controls the trade-off between fitting the data closely and maintaining smoothness in the estimated function. 08:46 🧮 Smoothing splines provide a smoother fit than cubic splines by constraining the average squared second derivative. 10:13 📈 Smoothing splines can be implemented in R with different values of lambda (spar) to control the smoothness of the estimated function. 13:31 🔄 Bootstrapping methods can be used to quantify uncertainty in smoothing spline estimates and create confidence regions for the curves. Made with HARPA AI
@AJ-et3vf2 жыл бұрын
Awesome video! Thank you!
@michaelho5138 Жыл бұрын
Great video!
@vincentole3 жыл бұрын
Great Video! Thank you :)
@user-zh2wk7yh2s2 жыл бұрын
Thanks a lot
@sillysully934 жыл бұрын
This is great
@virajmohile92944 жыл бұрын
Wonderful! Thanks!!
@nourailham25442 жыл бұрын
Can you give me your email! I have a question about this course.