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Instability in the predictor importance results working with small sample data.
The client is using Modeler's Linear Regression node. A model is fitted with three predictors. The predictor importance results are essentially the opposite of what the client expects based on the standardized regression coefficients
The algorithm does not produce the same results with different record ordering and the results can be unstable in small samples and investigations showed that the results he expects are given if you use a very large sample, but are inconsistent in smaller samples (his sample has 48 records).
The algorithm involves some randomness and really is designed for large samples. Importance measures from the LINEAR node should be used when using small sample, as these results do not vary and would provide the wanted outcome.
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