January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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H-measure
Binary classification has to apply techniques so that it can map independent variables to different labels. For example, a number of variables exist such as gender, income, number of existing loans, and payment on time/not, that get mapped to yield a score that helps us classify the customers into good customers (more propensity to pay) and bad customers.
Typically, everyone seems to be caught up with the misclassification rate or derived form since the area under curve (AUC) is known to be the best evaluator of our classification model. You get this rate by dividing the total number of misclassified examples by the total number of examples. But does this give us a fair assessment? Let's see. Here, we have a misclassification rate ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.