Hi, Thomas, sorry, I overread the regression part … This would be a bit trickier, I am not sure what a good strategy for averaging regression outputs would be. However, if you just want to compute the average, you could do sth like np.mean(np.asarray([r.predict(X) for r in list_or_your_mlps]))
However, it may be better to use stacking, and use the output of r.predict(X) as meta features to train a model based on these? Best, Sebastian > On Jan 7, 2017, at 1:49 PM, Thomas Evangelidis <teva...@gmail.com> wrote: > > Hi Sebastian, > > Thanks, I will try it in another classification problem I have. However, this > time I am using regressors not classifiers. > > On Jan 7, 2017 19:28, "Sebastian Raschka" <se.rasc...@gmail.com> wrote: > Hi, Thomas, > > the VotingClassifier can combine different models per majority voting amongst > their predictions. Unfortunately, it refits the classifiers though (after > cloning them). I think we implemented it this way to make it compatible to > GridSearch and so forth. However, I have a version of the estimator that you > can initialize with “refit=False” to avoid refitting if it helps. > http://rasbt.github.io/mlxtend/user_guide/classifier/EnsembleVoteClassifier/#example-5-using-pre-fitted-classifiers > > Best, > Sebastian > > > > > On Jan 7, 2017, at 11:15 AM, Thomas Evangelidis <teva...@gmail.com> wrote: > > > > Greetings, > > > > I have trained many MLPRegressors using different random_state value and > > estimated the R^2 using cross-validation. Now I want to combine the top 10% > > of them in how to get more accurate predictions. Is there a meta-estimator > > that can get as input a few precomputed MLPRegressors and give consensus > > predictions? Can the BaggingRegressor do this job using MLPRegressors as > > input? > > > > Thanks in advance for any hint. > > Thomas > > > > > > -- > > ====================================================================== > > Thomas Evangelidis > > Research Specialist > > CEITEC - Central European Institute of Technology > > Masaryk University > > Kamenice 5/A35/1S081, > > 62500 Brno, Czech Republic > > > > email: tev...@pharm.uoa.gr > > teva...@gmail.com > > > > website: https://sites.google.com/site/thomasevangelidishomepage/ > > > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
