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 <tevang3@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.raschka@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/...
Best, Sebastian
On Jan 7, 2017, at 11:15 AM, Thomas Evangelidis <tevang3@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: tevang@pharm.uoa.gr tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
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