Same as percentCorrectList but for weird heuristics that do not consistently choose the same row. When a symmetric heuristic predicts row1 > row2, then it also predicts row2 < row1. Those can be used with percentCorrectList. All heuristics built into heuristica qualify. They will get the same answers for percentCorrectList and percentCorrectListNonSymmetric. But a non-symmetric heuristic will only get correct answers for percentCorrectListNonSymmetric.
Data to try to predict. Must have same criterion column and cols_to_fit as the data heuristics were fit to.
A list of one or more heuristics fitted to data, e.g. the output of ttbModel.
A one-row data.frame of numbers from 0 to 100, the percent correc of each heuristic. Each column is named with the heuristic's class or the fit name.
percentCorrectList which is faster but wil only be accurate
for symmetric heuristics. (percentCorrectListNonSymmetric will be
accurate for both symmetric and non-symmetric heuristics, but it's slower.)
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