scikit-learn: machine learning in Python. Please feel free to ask specific questions about scikit-learn. Please try to keep the discussion focused on scikit-learn usage and immediately related open source projects from the Python ecosystem.
If you go there: https://github.com/JosephTLucas/scikit-learn/pulls and click "New Pull Request" it creates the PR against scikit-learn/scikit-learn from JosephTLucas fork and as you noticed I was not able to change that to use JosephTLucas fork as the target (edit it turns out clicking on the blue arrow between base and head fork is another work-around)...
I have sent the pull request to Joseph. Thanks for help.
It is the first time I send a pull request to a contributor to update his pull request to the main repo. If he accepts my pull request (to the branch of the pull request), will it automatically update the pull request (to the main repo) with my commit ?
It is the first time I send a pull request to a contributor to update his pull request to the main repo. If he accepts my pull request (to the branch of the pull request), will it automatically update the pull request (to the main repo) with my commit ?
Side-comment: if you collaborate from time to time with JosephTLucas a reasonable way to make that easier is that JosephTLucas gives you write access to his fork. This way you can push directly to his branch without doing a PR on his fork.
3 replies
i have a problem with a 1D-Classification.
I simplified my problem to the following:
There are 2 classes +1 and -1 and i have a trainvalue for each of the classes
0.0293294646367189 (class -1) and 0.025545042466768184 (class 1)
Now that i trained a LinearSVC with those values i throw some random values to the classifier to predict
I simplified my problem to the following:
There are 2 classes +1 and -1 and i have a trainvalue for each of the classes
0.0293294646367189 (class -1) and 0.025545042466768184 (class 1)
Now that i trained a LinearSVC with those values i throw some random values to the classifier to predict
i expect the decision limit to be in the center of the two example values
but even 0.025545042466768184, the train data for class 1 is predicted as -1
when I use LassoLarsCV(cv=5).fit(X_scaled, y[policy]) I get a score of 0
what am I doing wrong?
Its a regression problem
i am writing a neural network without any external libraries for MNIST with only 4 labels.In the train _labels.csv file i have one hot encoded data(1000) for all samples, my doubt is how to call the data directly to the function in the code. iam using softmax as my activation function in output layer
Hi @FranciscoPalomares Random Forest is a collection of models which can be trained independently and can be parallelised: I guess for the classification you are using the RandomForestClassifier, you can scale the training through the n_jobs parameter when you instantiate it, something like
RandomForestClassifier(n_estimators=100, n_jobs=-1)
I am not clear what a cluster means here
@adrinjalali (thank you)
yes, the examples and the user guide will give you a better idea on how it works and what it does: https://scikit-learn.org/dev/modules/clustering.html#hierarchical-clustering
I am surprised it works so well for my regression problem!
Hullo! I took on #9602 as a first issue for the MLH Fellowship. Looking into it further, it seems like the scope of the issue is much larger than clarifying a single docstring. I'm unsure of whether to start poking away at it, or whether to start a larger discussion on the direction of multiclass/multilabel learning in sklearn. Would love to hear thoughts. :)
In some places, multiclass/multilabel functionality is explicit and indicated by the name of the function.
can you give an example of that?
that's for meta-estimators, I guess?
I'll focus on clarifying the OVR docstring, then.
Does anyone have any tips for making an API proposal go smoothly? (e.g. the structure of a good proposal, good examples of existing proposals)
Context: I was hoping to start working on a proposal for adding Gibbs sampling to LatentDirichletAllocation, going off of Thomas's recommendations.
Ah, just found the SLEP template.
it can be just an issue like this one, for example scikit-learn/scikit-learn#15346
Hello, the question I meet is the socres returned from the function 'cross_val_score' seem to be very different from the result of actual fitting process. This is the last training process where most of the valid loss are about 0.1. However the score I get from the 'cross_val_score' is about 0.49. The score I use is the 'neg mse' which is the similar to the loss function of the network 'mse'. I want to know why it happens and how to fix it. Thanks a lot.
Upvote vscode issue to allow colorfull output when fitting with Sklearn. microsoft/vscode-python#12615
Can I request I/we/someone works on getting this merged scikit-learn/scikit-learn#15007 as part of the SciPy sprint
