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Is there currently support for continuous variable-type analyses in SCP? I know a lot of the various ML techniques like random forest support continuous variables. It seems the workflow shouldn't be too hard to adapt, if there isn't -- use the ROI tool but have a column for continuous variables instead of class variables when doing the extraction. Thoughts?
The text was updated successfully, but these errors were encountered:
@jgrn307 thank you, I'll consider adding ML regression to the algorithms.
Probably the input should be already a raster with continuous variables, because I think the ROI tool should be specific for thematic classifications.
So the training data (polygons or points) would contain the response variable as attributes, and the spectra would be extracted and "joined" to those points for a model to be built. ROIs are probably not the right term, exactly -- just vector data + attributes containing a continuous variable.
So the training data (polygons or points) would contain the response variable as attributes, and the spectra would be extracted and "joined" to those points for a model to be built. ROIs are probably not the right term, exactly -- just vector data + attributes containing a continuous variable.
Is there currently support for continuous variable-type analyses in SCP? I know a lot of the various ML techniques like random forest support continuous variables. It seems the workflow shouldn't be too hard to adapt, if there isn't -- use the ROI tool but have a column for continuous variables instead of class variables when doing the extraction. Thoughts?
The text was updated successfully, but these errors were encountered: