Scikit-Learn Model Pipeline Tutorial
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Пікірлер: 47
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Keep Posting Greg, I am Data Analyst by profession and your video certainly helps a lot
@GregHogg
2 жыл бұрын
That's awesome! Thank you 😄
Great stuff! I’m curious why you used FunctionTransformer instead of ColumnTransformer, which could run the two scalers in parallel? Also, since FunctionTransformer is stateless, the documentation says that fit just checks the input rather than actually fitting the scaling parameters. Doesn’t that lead to data leakage since applying transform to test data won’t use parameters learned from fitting on the training data?
Great tutorial! I use the MinMaxScaler with the option to scale from -1 to 1 instead of 0 to 1 when I am dealing with values that can be positive and negative. Seems to be fine, but I may need to reconsider going forward. I have never noticed any issues though.
A very practical video, that I came across on Pipelines. Thank you for this video!
@GregHogg
Жыл бұрын
Awesome that's great to hear. You're very welcome ☺️☺️
Thanks Greg. This made me realise how non-standard my code is. I learnt: - Use copy or deepcopy and not assignment. - Always perform preprocessing on the train and test separately. - sklearn pipelines have nothing to do with ETL pipelines from Data Engineering. - sklearn transfers have nothing to do with NLP Transformers. - sk elarn estimators have nothing to do with Statistics estimators.
@GregHogg
9 ай бұрын
Super glad you got some useful pointers!!
Thank you Greg! It's a great video!
@GregHogg
Жыл бұрын
Glad to hear it!
This was very helpful, thank you :)
I would love to see a tutorial that covers using pipelines with multilayer perceptron models (MLPs), CNNs and LSTMS.
Thank you for the video!
Thanks for the great tutorial. Can you make a video on how to combine multiple feature selection methods and feature extraction using python?
I undstand what you are doing here but I have two questions that I think would be helpful and would make it easier to follow along and replicate you steps. 1) Where did you get the data. I can't the california_housing dataset that is already in the train/test form. 2) Why not use scikit-learn tooling rather than doing it yourself? Like you could have used train/test split or pipelines (or column transformer... or similar stuff). That just has me confused.
How to transform y variable and then fit model. And after how to reverse transform for the scatter plotting
Perfect explanation. Thanks a lot
@GregHogg
3 ай бұрын
Very welcome 😁
TYSM bro really appreciate this
@GregHogg
6 ай бұрын
Very welcome!!
Thank you!
Thanks for the great tutorial! what do I need to change to create a pipeline for an image classification model? like the cifar10 model?
@GregHogg
Жыл бұрын
Well, everything. You probably won't be using scikit for that. And you're very welcome!
@talyb7383
Жыл бұрын
@@GregHogg I didnt explained myself clearly... I want to create a pipeline that receives a trained cifar10 model an also make preprocessing on the e data set ? so I cant use your way?
awesome ty
Thanks for this amazing video! Would that work also with a statsmodels model?
@GregHogg
6 ай бұрын
Thanks so much!! And I'm not sure, haven't tried :)
Great Video!
@GregHogg
Жыл бұрын
Thank you Adrian!
Just out of curiosity, is there a reason you don't use train_test_split to get X and y values?
@user-dh6wx3fe6y
6 ай бұрын
yes, why he uses X_train for train_predictions instead of another dataset X_valid
Awesome !
@GregHogg
7 ай бұрын
Thank you!
Bro can you show how to make youtube and any video downloader make by python
nice video Greg
@GregHogg
2 ай бұрын
Thanks so much!!
you are ❤
@GregHogg
2 жыл бұрын
❤️
Did you say pipelines doesn't function for classifications problems? Min: 1:07
@GregHogg
Жыл бұрын
Does, not doesn't
@fabio336ful
Жыл бұрын
@@GregHogg thanks 🙏🏼
Can you share this notebook?
@GregHogg
Жыл бұрын
dang i think i lost it, sorry
Bro you literally just copied this out of a textbook lmao but I respect the grind.
Too confusing. Too many tangents, doesn't cover the main idea clearly. Downvoted.
@GregHogg
5 ай бұрын
Well I upvoted it to counter you