Re: [scikit-learn] Does model consider about previous training results after reloading model and then training with new data?

> > > > I think the following could work if the estimators_ support partial_fit: > > voter = VotingClassifier(...) > voter.fit(...) > > For further training: > > for i in len(estimators_): > voter.estimators_[i].partial_fit(...) > > ok, maybe using Voting classifier to determine regression

Re: [scikit-learn] Does model consider about previous training results after reloading model and then training with new data?

> So if I want to reach like "continue training", I should choose model with > partial_fit, right? Yes. > but I saw nothing have partial_fit function in ensemble methods, Hm, technically, if the models in the ensemble support partial_fit the ensemble method itself should also be able to use

Re: [scikit-learn] Does model consider about previous training results after reloading model and then training with new data?

Sebastian Raschka 於 2019年2月1日 週五 下午1:48寫道： > Hi there, > > if you call the "fit" method, the learning will essentially start from > scratch. So no, it doesn't consider previous training results. However, certain algorithms are implemented with an additional partial_fit > method that would consid

Re: [scikit-learn] Does model consider about previous training results after reloading model and then training with new data?

Hi there, if you call the "fit" method, the learning will essentially start from scratch. So no, it doesn't consider previous training results. However, certain algorithms are implemented with an additional partial_fit method that would consider previous training rounds. Best, Sebastian > On

[scikit-learn] Does model consider about previous training results after reloading model and then training with new data?

As title, I'm confused. If I reload model and train with new data, what happened? 1st train old data -> save model -> reload -> train with new data Does the 2nd training will consider about previous training results? Or just a new result with new data? ___

Re: [scikit-learn] Bounded logistical regression in Python

I don't quite get your terminology, to "add a variable c to center an independent variable Xk", and you've got an extra ) in your equation, so I'm not sure exactly where you want it... If you mean P(X) = a / (1 + exp(b0 + b1*X1 + .. + bn*Xn) * (Xk - c)) then that's the same as P(X) = a / (1 + e

[scikit-learn] Bounded logistical regression in Python

Hi there! The standard logistical regression solver in scikit-learn assumes the regression equation: P(X) = 1/ (1 + exp(b0 + b1*X1 + ... + bn*Xn)) .. and solves for the b's using various solver routine