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Currently, I am exploring the concept of latent variable for regression type datasets.

I have gone through literature of few of the methods and models used in finding latent variable, like: EM algorithms, Partial least square regression, Latent semantic analysis, Mixed Effect models (linear-nonlinear), HMM, and there are many more!!

For Example:

volume DataFrame head is 
    length     width      volume
0  1.395702  4.822958    40.821677
1  5.761620  9.912682   242.571731
2  3.444930  2.111199    18.904144
3  6.236642  7.609429   425.838818
4  7.270517  1.106117    39.883937

In the above mentioned dataset, height, type-of-object, units and other parameters are missing. As mentioned by people previously, it is difficult to regenerate them as such but there is a possibility of acknowledging them using latent variable.

Hence, I would like to ask which method or model will be more suited for such kind of dataset?

Thanks!!

Mohammad Saad
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    Welcome to DataScienceSE. There's a bit of confusion in your question, for example EM is a method used in various types of models but it's not a model by itself. Generally there's no such thing as "the best model for everything", different models have different characteristics and different applications. Do you have a particular kind of problem in mind? – Erwan Apr 22 '21 at 22:27
  • @Erwan thanks for the kind response! Apologies for the vague question! Yeah I am trying to find hidden variables for a given dataset (linear regression). Hence, I want to know which model or method will be better to start with. As, I have seen few examples on the web but nothing particular to this type of problem. Please do advise and suggest!! – Mohammad Saad Apr 24 '21 at 04:14

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