This is the start node of a backward feature elimination loop. The first iteration of the loop is executed
with all input columns. In the next
n - 1
iterations each of the input columns - except the target column, that must be selected in the dialog - is
left out once. Then the end node will discard the column that influenced the prediction result the least.
n - 2
iterations follow where each of the remaining columns is left out once, and so on. The total number of
iterations is therefore
n * (n + 1) / 2 - 1.
The backward feature elimination loop can handle arbitrary target column, for numerical columns the squared error is taken as quality measure, for all other columns the error rate (i.e. the fraction of rows for which target and prediction column have different values).
This node differs from the 1-port node in that it has actually two ports on both sides. This makes it easier to feed training and test data into the flow.
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