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Green Version.pdf

RJ-2017-016.pdf
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🔗 Source: journal.r-project.org
📊 Size: 822 KB
📄 Pages: 16 pages
⬇️ Downloads: 301

Summary

Complex nonparametric models like neural networks and random forests are common in predictive analytics, but understanding their results can be challenging. Partial dependence plots offer a solution, providing low-dimensional graphical renderings of the prediction function to understand the relationship between the outcome and predictors. The pdp R package is introduced for constructing partial dependence plots, which can be applied to various classes of fitted models, including black box models like random forests and support vector machines. The package provides a general framework for constructing partial dependence plots, allowing for the visualization of the relationship between a subset of features and the response while accounting for the average effect of other predictors.

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Description

Complex nonparametric models like neural networks and random forests are common in predictive analytics, but understanding their results can be challenging.

Technical Information

  • File Format: PDF
  • File Size: 822 KB
  • Pages: 16
  • Language: EN
  • Total Downloads: 301
  • Last Updated: 2 weeks ago

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