Data Science: Tips and Tricks to Learn Data Science Theories Effectively by William Vance

Author:William Vance [Vance, William]
Language: eng
Format: epub
Published: 2020-02-26T17:00:00+00:00

False Positives

It is better to have a failure to classify than to have an improper classification. For instance, in a 2 ×2 scheme, i.e., a two-category n=2, every off-dimension matrix in the confusion matrix is a false positive. This implies that, when n >2, it means some classification errors are worse than the other.

Calculating the percentage of false positives is a very important metric to work with. This can be calculated by dividing the total classification undertaken by the weighted count or simple count of classification.

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