3 ms·

It's most certainly done for interpretability reasons. A non-linear model would provide huge lift here. Another option could be to run a non-linear model off to

by extr 3y ago

It's most certainly done for interpretability reasons. A non-linear model would provide huge lift here. Another option could be to run a non-linear model off to the side and understand the most powerful interaction terms and incorporate that into the linear model. This same problem is faced by insurance companies vs insurance regulators.

dr_kiszonka 3y ago
Right, but they can't really interpret this model, even if it does a good job at prediction. Two references, in case they ever read this :) 1. https://www.tandfonline.com/doi/abs/10.1080/09720502.2010.10700699 https://www.tandfonline.com/doi/abs/10.1080/09720502.2010.10... [1051 citations] 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/ [1032 citations]
- pas 3y ago
  for those less blessed with a priory knowledge or time (and/or motivation to chew through these articles) can I ask you for a tl;dr? maybe could you show an example where the linear model fails? and what's an non-linear model that would overcome this problem? (also I have no idea whether it's related, but ... my very sparse knowledge of this field just regurgitates the Statmodeling blog (by Andrew Gelman and co.), which is basically something something monte carlo + hierarchical models + calibration... but of course it's easy to shout buzzwords.)
  - extr 3y ago
    The tl;dr is multicollinearity is a big problem for interpreting models like this. Aka you have variables in the model that are highly correlated and because of this the model does not necessarily assign weights to the variables in a way that means anything. Eg: In the article they show "Age: 34 or Older" = -.11 and also that "Married" = -.49. Being over 34 and married is -.60. But as far as we know the model doesn't account for the fact that people older than 34 are more likely to be married in the first place. Perhaps the model would have worked just as well if "Age: 34 or Older" = -.30 and also that "Married" = -.30 (still -.60). And so on with every possible combination of attributes (they are all correlated). This isn't the end of the story. In situations where interpretability really matters or you have the time/resources, it's possible to mostly prevent this by building the model slowly and diagnosing multicollinearity issues as they arise. Perhaps you purposefully leave out variables because you see they are already highly correlated with existing ones and leave the model weights unstable. It requires exhaustive analysis and necessarily involves subjectivity, though. The researchers here could have done something like this, it's certainly done in industry for the aforementioned financial regulatory environments. Non-linear models like trees don't have multicollinearity problems because they implicitly model interactions between variables with the branching behavior. Instead the problem is interpretation, it's hard to sum up all those branches in a way that is intuitive and meaningful.

by extr 3y ago

dr_kiszonka 3y ago

Right, but they can't really interpret this model, even if it does a good job at prediction. Two references, in case they ever read this :) 1. https://www.tandfonline.com/doi/abs/10.1080/09720502.2010.10700699 https://www.tandfonline.com/doi/abs/10.1080/09720502.2010.10... [1051 citations] 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/ [1032 citations]

pas 3y ago
for those less blessed with a priory knowledge or time (and/or motivation to chew through these articles) can I ask you for a tl;dr? maybe could you show an example where the linear model fails? and what's an non-linear model that would overcome this problem? (also I have no idea whether it's related, but ... my very sparse knowledge of this field just regurgitates the Statmodeling blog (by Andrew Gelman and co.), which is basically something something monte carlo + hierarchical models + calibration... but of course it's easy to shout buzzwords.)
- extr 3y ago
  The tl;dr is multicollinearity is a big problem for interpreting models like this. Aka you have variables in the model that are highly correlated and because of this the model does not necessarily assign weights to the variables in a way that means anything. Eg: In the article they show "Age: 34 or Older" = -.11 and also that "Married" = -.49. Being over 34 and married is -.60. But as far as we know the model doesn't account for the fact that people older than 34 are more likely to be married in the first place. Perhaps the model would have worked just as well if "Age: 34 or Older" = -.30 and also that "Married" = -.30 (still -.60). And so on with every possible combination of attributes (they are all correlated). This isn't the end of the story. In situations where interpretability really matters or you have the time/resources, it's possible to mostly prevent this by building the model slowly and diagnosing multicollinearity issues as they arise. Perhaps you purposefully leave out variables because you see they are already highly correlated with existing ones and leave the model weights unstable. It requires exhaustive analysis and necessarily involves subjectivity, though. The researchers here could have done something like this, it's certainly done in industry for the aforementioned financial regulatory environments. Non-linear models like trees don't have multicollinearity problems because they implicitly model interactions between variables with the branching behavior. Instead the problem is interpretation, it's hard to sum up all those branches in a way that is intuitive and meaningful.