Re: [scikit-learn] scikit-learn twitter account

+1 for re-purposing Albert On Tue 5 Nov 2019 at 09:10, Guillaume Lemaître wrote: > We stopped twitting PR since January if I am not mistaken. So the current > channel does not have a real purpose :) > > On Tue, 5 Nov 2019 at 09:02, Roman Yurchak wrote: > >> Maybe re-purposing? I'm not sure if

Re: [scikit-learn] Vote on SLEP009: keyword only arguments

Hi all, Just a few comments about this SLEP from a contributor and user of the library :). I think it is important for users to be able to quickly and easily know/learn which arguments should be keyword arguments when they use scikit-learn. As a user, I do not want to have to double check each ti

Re: [scikit-learn] Variable kernel density estimation

Hi, The default score used by GridSearchCV is the one of the estimator; for KernelDensity it’s the total log likelihood. As far as I know it is not possible to have different bandwidths. Albert On Mon 8 Jul 2019 at 15:50, Naiping Dong wrote: > How sklearn perform cross validation "GridSearchC

Re: [scikit-learn] Isolation forests

Hi Sergio, In IsolationForest, BaseBagging is applied with ExtraTreeRegressor as base_estimator. Algorithm 2 (iTree) of the original paper is thus implemented in ExtaTreeRegressor. The forest is implemented thanks to the bagging procedure. HTH, Albert On Sun 20 May 2018 at 09:56, Sergio Solórza

Re: [scikit-learn] Error while running 'python setup.py build_ext --inplace'

Maybe run ‘make clean’ before running pip install ... Albert On Mon 4 Dec 2017 at 16:11, Aniket Meshram wrote: > I updated all the packages before running install. > > On Mon, Dec 4, 2017 at 6:07 PM, Olivier Grisel > wrote: > >> Maybe update your version of Cython? >> >> -- >> Olivier >> ​ >> >

Re: [scikit-learn] question for using GridSearchCV on LocalOutlierFactor

Hi, As Joel said LOF is not designed to be applied on unseen data. Therefore there is no public predict. Albert On Sun 8 Oct 2017 at 06:17, Joel Nothman wrote: > actually I'm probably wrong there, but you may not be able to use accuracy > ___ > sciki

Re: [scikit-learn] OneClassSvm | Different results on different runs

I opened an issue https://github.com/scikit-learn/scikit-learn/issues/9497 Albert On Thu, Aug 3, 2017 at 6:16 PM Andreas Mueller wrote: > > > On 08/03/2017 09:17 AM, Albert Thomas wrote: > > Yes, in fact, changing the random_state might have an influence on the > > result.

Re: [scikit-learn] OneClassSvm | Different results on different runs

ch could influence > the result if the tolerance parameter is too large? > > On Aug 3, 2017 1:28 PM, "Albert Thomas" wrote: > >> Hi Abhishek, >> >> Could you provide a small code snippet? I don't think the random_state >> parameter should influence

Re: [scikit-learn] OneClassSvm | Different results on different runs

Hi Abhishek, Could you provide a small code snippet? I don't think the random_state parameter should influence the result of the OneClassSVM as there is no probability estimation for this estimator. Albert On Thu, Aug 3, 2017 at 12:41 PM Jaques Grobler wrote: > Hi, > > The random_state paramet

Re: [scikit-learn] R user trying to learn Python

You can also have a look at "Effective Computation in Physics" by Anthony Scopatz and Kathryn D. Huff. It gives a very good overview of Python/numpy/pandas... Albert Thomas On Tue, 20 Jun 2017 at 07:25, C W wrote: > I am catching up to all the replies, apologies for the delay

Re: [scikit-learn] develop install with pip?

In fact `pip install --editable .` is the instruction given at the end of the Advanced installation instructions http://scikit-learn.org/stable/developers/advanced_installation.html#testing . I will submit a PR to recommend this in the Contributing section as well. Albert On Wed, May 31, 2017 a

[scikit-learn] develop install with pip?

Hi all, For a develop install it is suggested in the contributing section of the website http://scikit-learn.org/stable/developers/contributing.html to do: python setup.py develop However I read on stackoverflow that the preferred way to do this is now to use pip instead of using setuptools direc

Re: [scikit-learn] Multiple normal scenario for OCSVM

Hi Ady, Overfitting is a possible explanation. If your model learnt your normal scenarios too well then every abnormal data will be predicted as abnormal (so you will have a good performance for anomalies) however none of the normal instances of the test set will be in the normal region (so you w

Re: [scikit-learn] Problem with nested cross-validation example?

I also get "artifact not found". And I agree with Daniel. Once you decompose what the code is doing you realize that it does the job. The simplicity of the code to perform nested cross validation using scikit learn objects is impressive but I guess it also makes it less obvious. So making the exam

Re: [scikit-learn] Problem with nested cross-validation example?

When I was reading Sebastian's blog posts on Cross Validation a few weeks ago I also found the example of Nested cross validation on scikit-learn. At first like Daniel I thought the example was not doing what it should be doing. But after a few minutes I finally realized that it was correct. So I a

Re: [scikit-learn] Supervised anomaly detection in time series

Hi, About your question on how to learn the parameters of anomaly detection algorithms using only the negative samples in your case, Nicolas and I worked on this aspect recently. If you are interested you can have look at: - Learning hyperparameters for unsupervised anomaly detection: https://dri

Re: [scikit-learn] about svdd model

Hi, There was a pull request for the svdd https://github.com/scikit-learn/scikit-learn/pull/5899 But it has been closed recently... Note that if you apply the OCSVM with the rbf kernel it is equivalent to the svdd. Albert On sam. 23 juil. 2016 at 10:39, fengyanghe wrote: > Hi guys: > I'm try