pdp: An R Package for Constructing Partial Dependence Plots
TL;DR: Partial dependence plots as discussed by the authors are low-dimensional graphical renderings of the prediction function so that the relationship between the outcome and predictors of interest can be more easily understood, and are especially useful in explaining the output from black box models.
Abstract: 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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01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.
5,249 citations
TL;DR: Given the velocity of research on new machine learning models, it is preferable to have model-agnostic tools which can be applied to a random forest as well as to a neural network, to improve the adoption of machine learning.
Abstract: Complex, non-parametric models, which are typically used in machine learning, have proven to be successful in many prediction tasks. But these models usually operate as black boxes: While they are good at predicting, they are often not interpretable. Many inherently interpretable models have been suggested, which come at the cost of losing predictive power. Another option is to apply interpretability methods to a black box model after model training. Given the velocity of research on new machine learning models, it is preferable to have model-agnostic tools which can be applied to a random forest as well as to a neural network. Tools for model-agnostic interpretability methods should improve the adoption of machine learning.
328 citations
Journal Article•
TL;DR: A consistent collection of explainers for predictive models, a.k.a. black boxes, based on a uniform standardized grammar of model exploration which may be easily extended.
Abstract: Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand.
This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method irrespective of its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable.
Every explainer presented here works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different models and gives additional tools for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended.
197 citations
TL;DR: In this paper, a gradient boosting machine (GBM) was used to predict the slope stability of the circular slope in the R Environment software, trained and tested with the parameters obtained from the detailed investigation of 221 actual slope cases between 1994 and 2011 with circular mode failure available in the literature.
190 citations
TL;DR: Several methods to help subject-matter audiences (e.g., clinicians, medical policy makers) understand neural network models are described and step-by-step description on how to use these tools to facilitate better understanding of ANN are offered.
Abstract: Artificial neural networks (ANNs) are powerful tools for data analysis and are particularly suitable for modeling relationships between variables for best prediction of an outcome. While these models can be used to answer many important research questions, their utility has been critically limited because the interpretation of the "black box" model is difficult. Clinical investigators usually employ ANN models to predict the clinical outcomes or to make a diagnosis; the model however is difficult to interpret for clinicians. To address this important shortcoming of neural network modeling methods, we describe several methods to help subject-matter audiences (e.g., clinicians, medical policy makers) understand neural network models. Garson's algorithm describes the relative magnitude of the importance of a descriptor (predictor) in its connection with outcome variables by dissecting the model weights. The Lek's profile method explores the relationship of the outcome variable and a predictor of interest, while holding other predictors at constant values (e.g., minimum, 20th quartile, maximum). While Lek's profile was developed specifically for neural networks, partial dependence plot is a more generic version that visualize the relationship between an outcome and one or two predictors. Finally, the local interpretable model-agnostic explanations (LIME) method can show the predictions of any classification or regression, by approximating it locally with an interpretable model. R code for the implementations of these methods is shown by using example data fitted with a standard, feed-forward neural network model. We offer codes and step-by-step description on how to use these tools to facilitate better understanding of ANN.
167 citations
Cites background from "pdp: An R Package for Constructing ..."
...interest can be calculated as follows (18): (I) Suppose there are k observations, and i ∊ {1,2,3,....
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References
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TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
Abstract: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems
23,312 citations
"pdp: An R Package for Constructing ..." refers methods in this paper
...arrange(pdp1, pdp2, pdp3, ncol = 3) Note: the default color map for level plots is the color blind-friendly matplotlib (Hunter, 2007) ’viridis’ color map provided by the viridis package (Garnier, 2017)....
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...…zlab = "cmedv", drape = TRUE, colorkey = TRUE, screen = list(z = -20, x = -60)) # Figure 3 grid.arrange(pdp1, pdp2, pdp3, ncol = 3) Note: the default color map for level plots is the color blind-friendly matplotlib (Hunter, 2007) ’viridis’ color map provided by the viridis package (Garnier, 2017)....
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TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Abstract: Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.
17,764 citations
"pdp: An R Package for Constructing ..." refers background or methods in this paper
...…exception is regression trees based on single-variable splits which can make use of the efficient weighted tree traversal method described in Friedman (2001), however, only the gbm package seems to make use of this approach; consequently, pdp can also exploit this strategy when used with…...
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...This can be done in many ways, but in machine learning it is often accomplished by constructing partial dependence plots (PDPs); see Friedman (2001) for details....
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01 Jan 2007
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Abstract: Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.
14,830 citations
"pdp: An R Package for Constructing ..." refers methods in this paper
...Limited implementations of Friedman’s PDPs are available in packages randomForest (Liaw and Wiener, 2002) and gbm (Ridgeway, 2017), among others; these are limited in the sense that they only apply to the models fit using the respective package....
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Book•
29 Nov 2010
TL;DR: This tutorial jumps right in to the power of R without dragging you through the basic concepts of the programming language.
Abstract: Preface 1. Getting Started With R 2. Reading and Manipulating Data 3. Exploring and Transforming Data 4. Fitting Linear Models 5. Fitting Generalized Linear Models 6. Diagnosing Problems in Linear and Generalized Linear Models 7. Drawing Graphs 8. Writing Programs References Author Index Subject Index Command Index Data Set Index Package Index About the Authors
9,947 citations
"pdp: An R Package for Constructing ..." refers methods in this paper
...For example, the car package (Fox and Weisberg, 2011) contains many functions for constructing partial-residual and marginal-model plots....
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5,590 citations
"pdp: An R Package for Constructing ..." refers background in this paper
...Of course, since the default output produced by partial is still a data frame, the user can easily use any plotting package he/she desires to visualize the results—ggplot2 (Wickham, 2009), for instance (see Section 2....
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...Of course, since the default output produced by partial is still a data frame, the user can easily use any plotting package he/she desires to visualize the results—ggplot2 (Wickham, 2009), for instance (see Section 2.2.5 and Section 2.2.6 for examples)....
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