The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
pdp: An R Package for Constructing Partial Dependence Plots
Open accessAbstract
Complex nonparametric models-like neural networks, random forests, and support vector machines-are more common than ever in predictive analytics, especially when dealing with large observational databases that don't adhere to the strict assumptions imposed by traditional statistical techniques (e.g., multiple linear regression which assumes linearity, homoscedasticity, and normality).Unfortunately, it can be challenging to understand the results of such models and explain them to management.Partial dependence plots offer a simple solution.Partial dependence plots are lowdimensional graphical renderings of the prediction function so that the relationship between the outcome and predictors of interest can be more easily understood.These plots are especially useful in explaining the output from black box models.In this paper, we introduce pdp, a general R package for constructing partial dependence plots.
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“…package (Liaw and Wiener 2002), cross-validated model accuracy was assessed using the ‘caret’ package (Kuhn et al. 2019), predictor interaction importance was calculated with the ‘iml’ package (Molnar et al. 2018) and partial dependencies were calculated using the ‘pdp’ package (Greenwell 2017).”
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Metadata
- Authors
- Greenwell, B.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2017
- Volume
- 9
- Issue
- 1
- Pages
- 421
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2786693279
- Cited by
- 1,189
- References
- 6
- FWCI
- 23.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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