Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Visualisation to explain boosting #3

Open
MelanieIStefan opened this issue Apr 27, 2022 · 1 comment
Open

Visualisation to explain boosting #3

MelanieIStefan opened this issue Apr 27, 2022 · 1 comment

Comments

@MelanieIStefan
Copy link

Visualisations that may help explain boosting

I can think of two visualisations that may help with explaining boosting (though I am not sure how easy they would be to make).

One is to show the same image with the 6 trees next to each other, but colour them by how many points have been correctly classified and how many have been missed (so far) for each of the 6 trees. This would maybe help us better see what goes on in tree 6.

The second one is to create a composite image of all the decision boundaries that would illustrate the idea of the last step, where a weighted average is taken. Basically, there would be areas that are more or less orange and areas that are more or less blue, and it would (hopefully) be easy to see how the final decision surface comes about. (This may be an overly simplistic idea on my part, and it might turn out that this is not actually helpful. But may be worth a try).

Again, I could give these a shot.

@tompollard
Copy link
Collaborator

Thanks @MelanieIStefan I agree this would be a useful addition. If you have time to look I'd appreciate your help. I'm also happy to have a go if you'd like me to.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants