[HN Gopher] Explaining ML Models: A Non-Technical Guide to Inter...
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       Explaining ML Models: A Non-Technical Guide to Interpreting SHAP
       Analyses
        
       Author : sebg
       Score  : 28 points
       Date   : 2021-11-05 00:23 UTC (1 days ago)
        
 (HTM) web link (www.aidancooper.co.uk)
 (TXT) w3m dump (www.aidancooper.co.uk)
        
       | b9a2cab5 wrote:
       | This needs more emphasis on the "correlation is NOT necessarily
       | causation" part. In my experience trying to use SHAP and other
       | "interpretable ML" techniques for data science analysis, trying
       | to identify relevant drivers for a metric based on SHAP-like
       | techniques is a recipe for disaster. You'll get results that look
       | on the surface to someone that is well versed but not an expert
       | in the business like they're legit but to an expert they're
       | obvious garbage.
       | 
       | The way I and many others were taught in school is that you get
       | some dataset and you throw it into a predictor and get results,
       | but what they should've taught is how to find things (among
       | hundreds of possible attributes, or even attributes that aren't
       | collected yet) you think would be causal predictors first without
       | looking at the data and then explore and build a model based on
       | that.
        
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       (page generated 2021-11-06 23:02 UTC)