Re: [scikit-learn] MLPClassifier/Regressor and Kernel Processes when Multiprocessing

Hi, I cannot look too much in details. However, I would advice you to try using loky or joblib instead of multiprocessing, as a lot of work has been put in them to protect against problems that can arise in multi-process parallel computing (for instance the underlying numerical libraries may not b

Re: [scikit-learn] Voting software

True, but ideally it would need to be something more voting oriented that cannot be modified later on and archives a history of past decisions. On 27/04/2020 17:09, Hermes Morales wrote: https://doodle.com/es/ is not bad Obtener Outlook para Android ---

[scikit-learn] MLPClassifier/Regressor and Kernel Processes when Multiprocessing

Hi SciKit-Learn folks, I am building a stacked generalization classifier using the multilayer perceptron classifier as one of it's submodels. All data have been preprocessed appropriately and I am tuning each submodel's hyperparameters with a customized randomized search protocol (very similar to

Re: [scikit-learn] Vote: Add Adrin Jalali to the scikit-learn technical committee

+1 On Tue, 28 Apr 2020 at 01:34, Joel Nothman wrote: > +1 > > On Tue, 28 Apr 2020 at 02:23, Tom DLT wrote: > >> +1 >> >> Le lun. 27 avr. 2020, à 07 h 00, Alexandre Gramfort < >> alexandre.gramf...@inria.fr> a écrit : >> >>> +1 >>> ___ >>> scikit-learn

[scikit-learn] precision_recall_curve giving incorrect results on very small example

Here is a very small example using precision_recall_curve(): from sklearn.metrics import precision_recall_curve, precision_score, recall_score y_true = [0, 1] y_predict_proba = [0.25,0.75] precision, recall, thresholds = precision_recall_curve(y_true, y_predict_proba) precision, recall which resu