Re: [scikit-learn] how to preprocess in the cross_validate

Hi Oliver, Thanks for your kind reply. I read the manual, however, i did not find any options in the function of cross_validate to control the fit transformation. The fit_transform could be used to preprocessing in the pipeline, however, how to integrate this into the function of sklearn.model_s

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

[scikit-learn] Variable kernel density estimation

How sklearn perform cross validation "GridSearchCV" for bandwidth selection? It seems that the CV for kernel density estimation is different with the one used for classification. Is it used least square errors for this aim? Second, is it possible for me to use variable bandwidth for kernel density

[scikit-learn] Can I pre-calculate parameter threshold of Birch?

The threshold is determined by the sphere and simulate the points into a sphere. When I tune parameters, I don't know how to set the range of threshold to tune. Can I pre-calculate the threshold? ___ scikit-learn mailing list scikit-learn@python.org htt