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Last update: February 19, 2014

 

Cost Matrix

The cost matrix is an important component of most fitness functions for classification, logistic regression and logic synthesis. It allows you to attribute a cost to the four different classification outcomes, commonly shown in the confusion matrix: true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN). The cost associated with correct classifications is positive and negative for misclassifications. The default values GeneXproTools provides are a good neutral starting point, as they are based on the proportion of positive and negative records in the training dataset.

GeneXproTools evaluates the overall weight of the cost matrix and combines it with other fitness components, such as the area under the ROC curve or the R-square. The weight of the cost/gain balance was fine-tuned for each fitness function in order to ensure the best possible performance on a wide range of datasets.


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 Released February 19, 2014

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