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

Unlabeled data #13

Closed
hyjocean opened this issue Nov 3, 2021 · 2 comments
Closed

Unlabeled data #13

hyjocean opened this issue Nov 3, 2021 · 2 comments

Comments

@hyjocean
Copy link

hyjocean commented Nov 3, 2021

hello, thanks for your excellent work.
wmm, and I have a problem, I find the response in "starting with "wide" data", you say the data can be unlabeled, it depends on my task "(You don't need labels necessarily, depending on your task.)"
and when I read your article or code readme, I notice that you mentioned the parameters in different data are same, right? (ok, I don't know if I understand right, and I can't find where is the latter information.)
So my question is, could I apply your work on my unlabeled data? if it's true, how can I set the "Y_traing" in examples codes?
thanks!

@angus924
Copy link
Owner

Hi @hyjocean, sorry for the slow reply.

In relation to labels, I didn't mean any 'special', only that you won't need labels for certain tasks (e.g., clustering, unsupervised training, etc.).

You don't need the labels to generate the features (i.e., X_training_transform = transform(X_training, parameters)), but you do need the labels when training the classifier (e.g., classifier.fit(X_training_transform, Y_training)). If you don't have labels then you can't train the classifier in this way.

I hope that helps.

@hyjocean
Copy link
Author

Ahh, I get it.
Thanks for your patience reply~

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