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How to Optimize Meta-Cost Matrix
We have a classification process to attempt to predict infrequent events in a large dataset, and are using the meta-cost operator to place more value on the performance of these events (like 1 in 1,000) to minimize false negatives . Is there a method to optimize the cost matrix to class recall values, or does the user just need to iterate thru the cost matrix to arrive at acceptable values?