Overfitting

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Case study notes[1]

Introduction[edit]

Overfitting is when a statistical model takes too many parameters. (continue general meaning)

In Machine Learning, overfitting is when you take into account too many random factors. The problem lies within the random factors, as there is always some noise. (reference to define noise). When the AI overfits, it ends up learning from the noise, which might make decision making a lot worse.


References[edit]