[MINI] The Curse of Dimensionality
More features are not always better! With an increasing number of features to consider, machine learning algorithms suffer from the curse of dimensionality, as they have a wider set and often sparser coverage of examples to consider. This episode explores a real life example of this as Kyle and Linhda discuss their thoughts on purchasing a home.
The curse of dimensionality was defined by Richard Bellman, and applies in several slightly nuanced cases. This mini-episode discusses how it applies on machine learning.
This episode does not, however, discuss a slightly different version of the…
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