2021-12-13 21:14:44
[D] Perhaps Leo Brieman was not right in his criticism about 'Data Models' in his famous paper "Statistical Modeling : The two cultures"
Often the Auto ML or low code Data science library creators take refuge in the famous Leo Brieman's paper "Statistical Modeling : The two cultures". In it, he argues that statistician's fixation with Data models has led to :
* Led to irrelevant theory and questionable scientific conclusions
* Kept statisticians from using more suitable algorithmic models
* Prevented statisticians from working on exciting new problems
In his famous paper Leo Brieman gives an impression that Prediction is everything or as I infer it, one should just worry about predictive accuracy and perhaps one should not waste too much time trying to understand the Data generating process.
Fast forward 21 yrs, we find ourselves with cases like Zillow and other umpteen cases of Data science project failures where the goal was simply to " find an algorithm f(x) such that for future x in a test set, f(x) will be a good predictor of y.
I feel the mere fixation of trying to find the algorithm f(x) leads to people not focusing on the 'how' and 'Why' questions. The questions 'how' and 'why' are only asked post fact i.e. when the models fail miserably on the ground.
Given this background, don't you think Leo Brieman was wrong in his criticism about 'Data models'?
Would be happy to hear perspectives on both sides.
/r/statistics
https://redd.it/rewlsn
81 views18:14
D
