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.

Please write a clear, concise description of your topic here.You will likely reference your introduction from somewhere else. Please use the following syntax at the end of each of your ideas. IT IS CRITICAL YOU ATTRIBUTE others work. Your introduction should be factual. No more than 3 or 4 sentences, please. Because you are not an expert in your topic, I expect you to triangulate your information. LOTS OF LINK TO OTHER RESOURCES PLEASE!

 <ref> the url I cited by material from </ref>
 

How does it work or a deeper look[edit]

  • If you are discussing a THING YOU CAN TOUCH, you must explain how it works, and the parts it is made of. Google around for an "exploded technical diagram" of your thing, maybe like this example of an MRI It is likely you will reference outside links. Please attribute your work.
  • If you are discussing a PROCESS OR ABSTRACT CONCEPT (like fuzzy logic) you must deeply explain how it works.

Examples[edit]

Let's take an AI that looks at weather patterns. In the first 2 weeks it was active, it only rained on a Monday. If it overfits, the AI would make predictions that it would always and only rain on monday.

Please include some example of how your concept is actually used. Your example must include WHERE it is used, and WHAT IS BENEFIT of it being used.

Pictures, diagrams[edit]

Pictures and diagrams go a LONG way to helping someone understand a topic. Especially if your topic is a little abstract or complex. Using a picture or diagram is a two part process:

  1. upload a file
  2. use the file on a wiki page

External links[edit]

References[edit]