Stacking Ensemble Learning|Stacking and Blending in ensemble machine learning

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Stacking Ensemble Learning|Stacking and Blending in ensemble machine learning
#StackingEnsemble #StackingandBlending #unfolddatascience
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About this video:
In this video I explain the concepts of stacking and blending in ensemble learning. I discuss in detail the working methodology of stacking and blending in ensemble machine learning. Below topics are mentioned in this video:
1. What is stacking ensemble learning
2. Stacking and blending in machine learning
3. Stacking ensemble learning in python
4. What is meta classifier in stacking ensemble
5. Stacking meta classifier explanation

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First of all thanks for the video.
Bagging: when we take different models and train them parallely with each getting sub-set if data from the total data and each model has high-variance and low bias.
Boosting: same as above but the difference is instead of training them parallely, output of one model is given as input to the other and each model should have high-bias and low-variance.

atomicbreath

I've been struggling to understand this for quite a few hours now. Finally, got it. Thank you so much!

crazycurlzXD

That is very nicely Explained. Thank you, Sir.

TheMuktesh

Hello Aman Sir, Thank you for the great video, simple explanation.
Could you please elaborate on how the meta-model is built and used for the testing / real-test set?
Like here, the meta-model uses Logistic Regression, right? How a logistic regression works to stack the results from base model?

sharatainapur

Great explanation of the concept. Thank you for also showing the python samples to really bring it home.

davidgao

I am a new in this field and I was trying to understand this concept, refered many webpages and seen many videos. You explain Very nicely. I got it the concept.

hetal

Big fan, i didn't understand before, but now 🎉🎉🎉 u made my day

ShyamShyam-rnlr

I beg my pardon....I was struggling with this technique
Very clearly understood and the code n got executed!!
Thanks a lot

sridattu

In bagging, we make different subset of dataset by using row sampling and replacement and that subset we pass different model's to make prediction and at the end we combine or aggregate all of the model prediction.

saurabhdeokar

Thanks a lot... Was struggling with this Stacking approach.... Now it's clear!

ranajaydas

I love that you throughly explained the theory before you dove into the code. Great job!

tosinlitics

Can you make a separate video for Blending with detailed example and implementation without the libraries?

SandeepSSMishra

Hi Aman, thanks for your explanation! I have a question though - is regularization and ensembling the same? In the decision trees case we use the same techniques of bagging and boosting, so, if i'm regularizing am i implicitly ensembling and viceversa?
Thank you!

eduardocasanova

Very well explained. Can you also explain KcrossK cross validations and go in dept of meta model.

pranitflora

Can we do level 2 meta model. Also can we insert new training features in meta model?

MegaBoss

Thnks 4 the video, sir, can i perform stacking between different CNN models and feature fusion in between these models

seema

How to use stacking regressor models from sklearn and keras??

samuelpradhan

Bagging is Bootstrap Aggregation which is used primarily to reduce Variance, it uses CLT to do the same. Boosting improves the base learners by learning from mistake of the previous model using homogeneous weak learners, it helps in reducing Bias.

bharatbajoria

Sir Namaskar. That code you did in Python is for stacking or blending, kindly say.

SandeepSSMishra

Bagging helps in reducing variance due to overfitting in decision trees and further to reduce bias, boosting is used. Hence, ultimately we achieve a model with low bias and low variance.

vaddadisairahul

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