Tune multiple models simultaneously with GridSearchCV

[@dataschool]

Thanks for watching! 🙌 If you're brand new to GridSearchCV, I recommend starting with this tutorial instead: ua-cam.com/video/Gol_qOgRqfA/v-deo.html

[@Lumcoin]

OMG, a classic "UA-camr explains how to add 1+1" BUT NO ONE ELSE SAYS HOW TO ADD 1+1!!! Thanks a bunch, you most likely saved me two hours of frustrating trial and error.

[@dataschool]

Happy to help! 🙌

[@hemanthkotagiri8865]

I was literally on my hunt to see if I can train 4 different models with different parameters all at once and here you upload this. Perfect timing! Thanks, man! Love your content!

[@dataschool]

That's awesome to hear! So glad I could be helpful, and thanks for your kind words 🙏

[@gauravmalik3911]

Amazing video, there are very few videos on these such unique topics on UA-cam.
Had one doubt, didn't understood the placeholder part at 2:15.

[@AhmedThahir2002]

Amazing video!
Does this work for RandomizedSearchCV as well?

[@wayfaring.stranger]

You're a good boy; this has streamlined my PhD's research.

[@dataschool]

Thank you!

[@bakingbaker]

I don't doubt this works, but it seems a bit odd to specify an instantiated classifier in the pipeline only to override it with another instantiated classifier. Is there a way to make the classifier in the pipeline a generic placeholder?

[@DanielWeikert]

If you wrap it into a loop

[@bakingbaker]

@@DanielWeikert No need; I realized my question is kinda silly considering that it's no different then simply overriding default parameters. Instead of specifying attributes, each classifier is an object.

[@amarsharma336]

Thanks for saving our time, i used to do loops

[@dataschool]

You're very welcome!

[@marinapachecovillaschi2367]

Nice tip!!
As I'm solving a multi-output classification problem I'm using MultiOutputClassifier() for that and I think that's what's messing my code up when trying to run this solution. It looks something like this:
pipeline = Pipeline([
('vect', CountVectorizer(tokenizer=tokenize)),
('tfidf', TfidfTransformer()),
('classifier', MultiOutputClassifier(lr_clf))
])
# parameter dict for logistic regression
params_lr = {
'vect__decode_error' : ['strict', 'ignore', 'replace'],
'tfidf__norm' : ['l1', 'l2'],
'classifier__estimator__penalty' : ['l1', 'l2'],
'classifier__estimator__C' : [0.1, 1, 10],
'classifier__estimator' : [lr_clf]
}
# other dicts for different models
# list of parameters dicts
parameters = [params_lr, params_svc, params_rf]
cv = GridSearchCV(pipeline, param_grid = parameters, n_jobs=-1)
cv.fit(X_train, y_train)
Any tips on this? I think the gridsearch doesn't understand the MultiOutputClassifier.
Thanks in advance!!

[@Amir-gi5fn]

Thanks, helpful

[@dataschool]

You're welcome!