Prediction Modeling Methodology
- PMID: 31314250
- Bookshelf ID: NBK543534
- DOI: 10.1007/978-3-319-99713-1_8
Prediction Modeling Methodology
Excerpt
In the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.
Copyright 2019, The Author(s).
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References
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- Goodman S. A dirty dozen: twelve P-value misconceptions. Semin Hematol. 2008;45(3):135–40. - PubMed
-
- Kuhn M, et al. Caret: classification and regression training, 2016.
-
- Pedregosa F, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.
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- Anaconda distribution: The most popular Python/R data science distribution. [Online]. Available: https://www.anaconda.com/distribution/.
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