Data Science for Business by Foster Provost & Tom Fawcett

Author:Foster Provost & Tom Fawcett
Language: eng
Format: epub, pdf
Tags: COMPUTERS / Data Modeling & Design
Publisher: O’Reilly Media
Published: 2013-07-29T04:00:00+00:00

When working with a classifier that gives scores to instances, in some situations the classifier decisions should be very conservative, corresponding to the fact that the classifier should have high certainty before taking the positive action. This corresponds to using a high threshold on the output score. Conversely, in some situations the classifier can be more permissive, which corresponds to lowering the threshold.[43]

This introduces a complication for which we need to extend our analytical framework for assessing and comparing models. The Confusion Matrix stated that a classifier produces a confusion matrix. With a ranking classifier, a classifier plus a threshold produces a single confusion matrix. Whenever the threshold changes, the confusion matrix may change as well because the numbers of true positives and false positives change.

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