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ML search is more about ‘averages’ based on samples. Innovations like these are more about ‘shocks’ that surface fitting cannot capture. Note universal approx
by richrichie 2y ago
ML search is more about ‘averages’ based on samples.
Innovations like these are more about ‘shocks’ that surface fitting cannot capture.
Note universal approximation theorem applies only to smooth surfaces.
- tomrod 2y agoNot always. Quantile regression exists. And you can develop "no match" categories.
- richrichie 2y agoQuantile regression is also about averages.
- tomrod 2y agoAverages are formulated as measures of centrality in the L2 norm ("straight line" distance), sum(values) / count(values). Quantile regression uses modifications the L1 norm ("city block" distance); if median (50%) then it is a measure of centrality. Not everything is an average. If you're interested, this is a good (but math heavy) treatment: https://en.wikipedia.org/wiki/Quantile_regression#Computation_of_estimates_for_regression_parameters https://en.wikipedia.org/wiki/Quantile_regression#Computatio...
- kylehotchkiss 2y agoWell said.
- radarsat1 2y agoBut the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers.