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I suppose there are two types of composition here - homogeneous and inhomogeneous, which might be implemented differently:
homogeneous: all columns of target have the same scitype and we are we are defining our measure as a n-product of a single fixed uni-target measure m. Needed for time-series.
inhomogeneous: any two measures (multi-target or uni-target), one can take their product m1 x m2, to get a new multi-target measure .
We should keep in mind that multi-target predictions are tables.
The text was updated successfully, but these errors were encountered:
Also, one may want to specify per-dimension weights, eg for weighting time series forecasts differently depending on how far in future the forecast is.
I suppose there are two types of composition here - homogeneous and inhomogeneous, which might be implemented differently:
homogeneous: all columns of target have the same scitype and we are we are defining our measure as a n-product of a single fixed uni-target measure
m
. Needed for time-series.inhomogeneous: any two measures (multi-target or uni-target), one can take their product
m1 x m2
, to get a new multi-target measure .We should keep in mind that multi-target predictions are tables.
The text was updated successfully, but these errors were encountered: