Thanks for watching! 🙌 If you're brand new to GridSearchCV, I recommend starting with this tutorial instead: kzfaq.info/get/bejne/fdWcktSAzLjVlnU.html

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!

OMG, a classic "KZfaqr 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.

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

Thanks for saving our time, i used to do loops

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

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!!

Thanks, helpful

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?