Dear Moh Smith,

I suggest you take a look at the following paper, which illustrates the inductive biases that so-called correlations can sometimes induce. This might be an avenue to explore:

• Ng et al., "Solving the Stereo Correspondence Problem with False Matches", 2019 -

  Article Solving the stereo correspondence problem with false matches

Regards

Dear Moh Smith ,

“Machine Learning” is one of the most trending buzzwords. It is predominant in every industry sector as it empowers various organizations with innovative solutions to automate and increase the efficacy of products by reducing human intervention. You might have heard about the applications of weather forecasting, spam classification, or stock price prediction applications, so what exactly do these applications use? Yes, you are right. They are built using Machine Learning algorithms. These algorithms majorly fall into two categories - supervised algorithms and unsupervised algorithms. While supervised algorithms comprise data with labels, unsupervised algorithms have unlabelled data.

https://www.projectpro.io/article/classification-vs-regression-in-machine-learning/545

Regards,

Shafagat

Moh Smith Tale a look https://www.projectpro.io/article/classification-vs-regression-in-machine-learning/545

Yes. It is possible. I suggest you to try stacking ensemble method. You can check my following project: www.kaggle.com/code/staniherstaniher/stacking-regression-los-patient

If You want to stack multiple models, you can use :

sklearn.ensemble.StackingClassifier

It depends. For the case of regression, you can use stacking regressor or Mlxtend library

Your question is a bit too simple, to really understand what you are looking for, nor do you show any details of your model building approach etc.

In principle it all depends on your use case. Of course you can stack ML regression models for different purposes. You can even stack different techniques like PCA and ML or what ever you like, but you need to be aware that any modelling is a way of simplification your observed reality and there might be a threshold, where you loose significance when you pass it. But this really depends on your data and underlying observed system.

That is very risky! Better use your original data in the last model, I'd say.