Hey lampahome,
I'm currently working on an online learning library called creme:
https://creme-ml.github.io/. Each estimator and transformer has a
fit_one(x, y) method so that you can learn from a stream of data. I've
only been working on it for a bit less than a month now but it might
be of inter
Hi! I want to be able to run each fold of a k-fold cross validation fold in
parallel, using all of my 6 CPUs at once. My model is a hidden markov model and
I want to train it using the training portion of the data and then extract the
anomaly score (negative log-likelihood) of each test sequence
Hey all.
Should we collect some discussion points for the sprint?
There's an unusual amount of core-devs present and I think we should
seize the opportunity.
Maybe we should create a page in the wiki or add it to the sprint page?
Things that are high on my list of priorities are:
* slicing
Yes, I was thinking the same. I think there are some other core issues to
solve, such as:
* euclidean_distances numerical issues
* commitment to ARM testing and debugging
* logistic regression stability
We should also nut out OPTICS issues or remove it from 0.21. I'm still keen
on trying to work
Do you have a reference for the logistic regression stability? Is it
convergence warnings?
Happy to discuss the other two issues, though I feel they seem easier
than most of what's on my list.
I have no idea what's going on with OPTICS tbh, and I'll leave it up to
you and the others to decid
Convergence in logistic regression (
https://github.com/scikit-learn/scikit-learn/issues/11536) is indeed one
problem (and it presents a general issue of what max_iter means when you
have several solvers, or how good defaults are selected). But I was sure we
had problems with non-determinism on som
