Who Cited It

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

2014 · Journal of Computational and Graphical Statistics · 1,838 citations · 8 from inside this corpus

Alex Goldstein low, Adam Kapelner, Justin Bleich low, Emil Pitkin low

This article presents individual conditional expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. Classical partial dependence plots (PDPs) help visualize the average partial relationship between the predicted response and one or more features. In the presence of substantial interaction effects, the partial response relationship can be heterogeneous. Thus, an average curve, such as the PDP, can obfuscate the complexity of the modeled relationship. Accordingly, ICE plots refine the PDP by graphing the functional relationship between the predicted response and the feature for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate, suggesting where and to what extent heterogeneities might exist. In addition to providing a plotting suite for exploratory analysis, we include a visual test for additive structure in the data-generating model. Through simulated examples and real datasets, we demonstrate how ICE plots can shed light on estimated models in ways PDPs cannot. Procedures outlined are available in the R package ICEbox.

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual C… (2014)Peeking Inside the Black Box:…Random Forests (2001)Random ForestsGreedy function approximation: A gradient boosting machine. (2001)Greedy function approximation…Classification and Regression by randomForest (2007)Classification and Regression…Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) (2001)Statistical Modeling: The Two…Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe… (2019)Explainable Artificial Intell…Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018)Peeking Inside the Black-Box:…A survey of methods for explaining black box models (2019)A survey of methods for expla…Explainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023)Interpreting Black-Box Models…Machine Learning Interpretability: A Survey on Methods and Metrics (2019)Machine Learning Interpretabi…Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw… (2023)Explainable Artificial Intell…Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models (2020)Visualizing the Effects of Pr…
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What this paper cites, inside the corpus

PaperYearCited
Random Forests2001131,109
Greedy function approximation: A gradient boosting machine.200130,137
Classification and Regression by randomForest200718,420
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)20014,362

What cites it, inside the corpus

PaperYearCited
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challe…20199,796
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)20186,166
A survey of methods for explaining black box models20194,992
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence20231,981
Machine Learning Interpretability: A Survey on Methods and Metrics20191,822
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustw…20231,679
Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models20201,450

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Topics

Data Analysis with RComputer Science
Statistical Methods and InferenceMathematics
Mental Health Research TopicsPsychology

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