How to stack machine learning models in Python

In this video, I will show you how to combine several machine learning models into a single and robust meta-classifier via model stacking (also known as stacking model).

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#stacking #python #scikitlearn #modelstacking #stackingmodel #66daysofdata #datascience #machinelearning #dataprofessor #textmining

[paulntalo]

Thank you professor, your contribution towards enablement for data scientists is unmatched in this year. Your my best channel towards full stack data science

[gguchristine]

I was literally thinking about how to do stacking and I saw your video in my subscription box haha thanks for the video!

[TinaHuang]

Wow this is great - was just thinking about this couple days back!

[Julio_Zambrano]

Amazing!
This video is super helpful!
Thank you, Professor! :D

[connectrRomania]

Man your videos are awesome, keep up the good work. Thank you

[mamacita]

Thank you! Quick question, why do you perform train-test split if you’re going to use cross validation? Wouldn’t the cross validation do the split ?

[shubhamdandekar]

Thanks for this video now i understand what stacking is.

[mogamoga]

sir, why always logistic regression is used for stacking?

[transferlearning]

Tanks a lot for this helpful video, i was wondering on how we can use a loaded models(already pre-trained) as estimators ?

[yaminadjoudi]

thank you for this, is the stacking is the same concept as Modular neural networks MNNs please ?

[gammetube]

Thank Dear. how we check Cv of the model and save the developed model and check the evaluation of the model?

[tsunamio]

Could you make an example of this with Keras neural networks? This is a very specific issue when you wrap your DNN with KerasClassifier, where you must provide the new model as a function or something... instead of training it.

[MegaBoss]

Hi. Can we do level 2 meta model? Any references? Also can we insert new training data in meta model? Any references if yes?

[zuxfcnd]

One of the most useful videos in my life! Sure wont be able to find more convenient explanation of models stacking. Thank you professor! But I have a question, is it possible to visualize feature importances after stacking?

[ashwinig]

hello sir its was wonderful video very informative
sir can u please suggest me the best denoising network can we stack different denoising algorithms in same manner?

[hareshk.mangtaniretrita]

Great video! Had a quick question for you. After training and testing the KNN algorithm, how are the metrics of the performance of the test set higher than that of the training set? Haven't we trained the model using the training set? I would expect the model to be more accurate when making predictions in the training set (which is seen data) as opposed to the test set (unseen data). Regards!

[Karenshow]

Can we use the stacking on Time Series Models??

[Kmysiak]

Whats your logic for using log classifier final_estimator? How come you didn't tune your hyperparameters? Good clean code and well explained but could be better.

[hbggozb]

How are you able to obtain features of importance from the stacked model

[SadTeddyBeer]

At the end of the video, when we print the final df with metric scores in it, we can see that the stack model is mainly inspired of the random forest classifier. Why not go to the decision tree who has 1 to all its scores ? It's because 1 is likely to be a biased and so the stack classifier doesn't take it in count ?