November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Regularization
Regularization is one possible approach that a data scientist may use for improving the results generated from a statistical model or data science process, such as when addressing a case of overfitting in statistics and data science.
We defined fitting earlier in this chapter (fitting describes how well a statistical model or process describes a data scientist's observations). Overfitting is a scenario where a statistical model or process seems to fit too well or appears to be too close to the actual data.
Overfitting usually occurs with an overly simple model. This means that you may have only two variables and are drawing conclusions based on the two. For example, using our previously mentioned example of daffodil sales
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