May 2020
Intermediate to advanced
530 pages
17h 8m
English
Content preview from Hands-On Machine Learning with C++
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Pearson's correlation coefficient
This measure is a classic coefficient, which is quite applicable when comparing vectors. Its primary disadvantage is that when the intersection is estimated as low, then the correlation can be high by accident. To combat accidental high correlation, you can multiply by a factor of 50/min (50, rating intersection) or any other damping factor, the effect of which decreases with an increasing number of estimates. An example is shown here:
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.