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You can fit a linear regression with just a few points, technically if you have one more data point than regressors, it works. Because you've assumed a linear relationship with normally distributed errors. And you can interpret the output, because the values of the regression coefficients tell you something. "having a high score on X doubles the odds of outcome Y", for example. Also, because you've assumed a structure to the data, you can more easily test if the data has deviated from that structure. This can be data drift or single outliers-- for example, GARCH models (a type of regression) allow the normal distribution of the error to have a varying variance, so you can detect different variance regimes. In short, they help a human understand and interpret data. From what little I know, ML is not so good at that. But it has other advantages, and you don't always need or want the understanding. If your want to i.e. detect ground cover in satellite images, then all you care about is valid outputs, not necessarily the importance of near-infrared vs red band. And ML (can) beat regression models by providing better interpolation, by better handling regions of the data space which violate the assumptions of the regression model, etc. So it is a tradeoff. Both approaches are highly performant, just at different tasks. |
Some of the regression coefficients were negative, which means that increasing performance in, say, long jump, leads to shorter shot-put (I don't remember which throw the model was for) distances.
The model and approach looked understandable and "weird" at the same time. From a purely statistical perspective it makes sense, since that was the results coming from, I assume, maximum likelihood estimation. From a predictive performance, retrospectively it surely worked because it gave good prediction on past data, assuming there were training and test data (most likely, they were not, but let's assume).
But from a future prediction perspective, i.e. the forecasting and thus the manipulation of training to obtain a certain performance, did it make sense? I am very confident it did not, because, among other things, the performances of auxiliary lifts/feats were not independent (you cannot work on a heavier one rep max in the power clean and hope or work toward a shorter long jump performance).
The model by itself might have accurate, but considering that interpretable and thus guiding changes in the training program would have been a quite naive mistake. This kinda mistake is quite common among many who think too much about the machinery of the model and way too little about the domain.