May 2019
Intermediate to advanced
162 pages
4h 24m
English
Content preview from Supervised Machine Learning with Python
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Error terms
One of the central topics of model building is reducing error. However, there are several types of errors, two of which we have control over to some extent. These are called bias and variance. There is a trade-off in the ability for a model to minimize either bias or variance, and this is called the bias-variance trade-off or the bias-variance dilemma.
Some models do well at controlling both to an extent. However, this is a dilemma that, for the most part, is always going to be present in your modeling considerations.
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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.