top | item 40593358 (no title) markburns | 1 year ago That seems an uncharitable view of the reply.The search space is huge, we sometimes find needles in haystacks by accident, isn’t it exciting that we have tools now that can systematically check every piece of hay? discuss order hn newest richrichie|1 year 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|1 year ago Not always. Quantile regression exists. And you can develop "no match" categories. load replies (1) radarsat1|1 year ago But the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers. kylehotchkiss|1 year ago Well said.
richrichie|1 year 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|1 year ago Not always. Quantile regression exists. And you can develop "no match" categories. load replies (1) radarsat1|1 year ago But the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers. kylehotchkiss|1 year ago Well said.
tomrod|1 year ago Not always. Quantile regression exists. And you can develop "no match" categories. load replies (1)
radarsat1|1 year ago But the better the mean surface is fitted (in a generizable way), the easier it is to spot outliers.
richrichie|1 year ago
Innovations like these are more about ‘shocks’ that surface fitting cannot capture.
Note universal approximation theorem applies only to smooth surfaces.
tomrod|1 year ago
radarsat1|1 year ago
kylehotchkiss|1 year ago