June 2020
Intermediate to advanced
382 pages
11h 39m
English
Content preview from 40 Algorithms Every Programmer Should Know
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Variance
Variance quantifies how accurately a model estimates the target variable if a different dataset is used to train the model. It quantifies whether the mathematical formulation of our model is a good generalization of the underlying patterns.
Specific overfitted rules based on specific scenarios and situations = high variance, and rules that are generalized and applicable to a variety of scenarios and situations = low variance.
Our goal in machine learning is to train models that exhibit low bias and low variance. Achieving this goal is not always easy and usually keeps data scientists awake at night.
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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.