Performance evaluation of learning algorithms - Mohak Shah
Performance evaluation of learning algorithms - Mohak Shah
Performance evaluation of learning algorithms - Mohak Shah
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Recent Developments II: The H Measure I<br />
A criticism <strong>of</strong> the AUC was given by [Hand, 2009]. The argument<br />
goes along the following lines:<br />
The misclassification cost distributions (and hence the skew-‐<br />
ratio distributions) used by the AUC are different for different<br />
classifiers. Therefore, we may be comparing apples and oranges<br />
as the AUC may give more weight to misclassifying a point by<br />
classifier A than it does by classifier B<br />
To address this problem, [Hand, 2009] proposed the H-‐Measure.<br />
In essence, The H-‐measure allows the user to select a cost-‐weight<br />
function that is equal for all the classifiers under comparison and<br />
thus allows for fairer comparisons.<br />
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