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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Cites
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Predictive learning via rule ensembles
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Moustafa, N., Koroniotis, N., Keshk, M., Zomaya, A. Y., & Tari, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3280465 · Cited by 266
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211
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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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