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 low-dimensional graphical renderings of the prediction function $$\widehat{f}\left(\boldsymbol{x}\right)$$ 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 vignette, we introduce pdp (Greenwell 2017), a general R package for constructing partial dependence plots (PDPs) (Friedman 2001).

## Introduction

Harrison and Rubinfeld (1978) were among the first to analyze the well-known Boston housing data. One of their goals was to find a housing value equation using data on median home values from $$n = 506$$ census tracts in the suburbs of Boston from the 1970 census; see Harrison and Rubinfeld (1978), Table IV, for a description of each variable. The data violate many classical assumptions like linearity, normality, and constant variance. Nonetheless, Harrison and Rubinfeld (1978) (using a combination of transformations, significance testing, and grid searches) were able to find a reasonable fitting model ($$R^2 = 0.81$$). Part of the payoff for there time and efforts was an interpretable prediction equation which is reproduced in below.

$\widehat{\log\left(MV\right)} = 9.76 + 0.0063 RM^2 + 8.98\times10^{-5} AGE - 0.19\log\left(DIS\right) + 0.096\log\left(RAD\right) \\ - 4.20\times10^{-4} TAX - 0.031 PTRATIO + 0.36\left(B - 0.63\right)^2 - 0.37\log\left(LSTAT\right) \\ - 0.012 CRIM + 8.03\times10^{-5} ZN + 2.41\times10^{-4} INDUS + 0.088 CHAS \\ - 0.0064 NOX^2$

Nowadays, many supervised learning algorithms can fit the data automatically in seconds—typically with higher accuracy. The downfall, however, is some loss of interpretation since these algorithms typically do not produce simple prediction formulas like the one above. These models can still provide insight into the data, but it is not in the form of simple equations. For example, quantifying predictor importance has become an essential task in the analysis of “big data”, and many supervised learning algorithms, like tree-based methods, can naturally assign variable importance scores to all of the predictors in the training data.

While determining predictor importance is a crucial task in any supervised learning problem, ranking variables is only part of the story and once a subset of “important” features is identified it is often necessary to assess the relationship between them (or subset thereof) and the response. This can be done in many ways, but in machine learning it is often accomplished by constructing PDPs. PDPs help visualize the relationship between a subset of the features (typically 1-3) and the response while accounting for the average effect of the other predictors in the model. They are particularly effective with black box models like random forests and support vector machines.

Let $$\boldsymbol{x} = \left\{x_1, x_2, \dots, x_p\right\}$$ represent the predictors in a model whose prediction function is $$\widehat{f}\left(\boldsymbol{x}\right)$$. If we partition $$\boldsymbol{x}$$ into an interest set, $$\boldsymbol{z}_s$$, and its compliment, $$\boldsymbol{z}_c = \boldsymbol{x} \setminus \boldsymbol{z}_s$$, then the “partial dependence” of the response on $$\boldsymbol{z}_s$$ is defined as

$f_s\left(\boldsymbol{z}_s\right) = E_{\boldsymbol{z}_c}\left[\widehat{f}\left(\boldsymbol{z}_s, \boldsymbol{z}_c\right)\right] = \int \widehat{f}\left(\boldsymbol{z}_s, \boldsymbol{z}_c\right)p_{c}\left(\boldsymbol{z}_c\right)d\boldsymbol{z}_c,$

where $$p_{c}\left(\boldsymbol{z}_c\right)$$ is the marginal probability density of $$\boldsymbol{z}_c$$: $$p_{c}\left(\boldsymbol{z}_c\right) = \int p\left(\boldsymbol{x}\right)d\boldsymbol{z}_s$$. The above equation can be estimated from a set of training data by

$\bar{f}_s\left(\boldsymbol{z}_s\right) = \frac{1}{n}\sum_{i = 1}^n\widehat{f}\left(\boldsymbol{z}_s,\boldsymbol{z}_{i, c}\right),$

where $$\boldsymbol{z}_{i, c}$$ $$\left(i = 1, 2, \dots, n\right)$$ are the values of $$\boldsymbol{z}_c$$ that occur in the training sample; that is, we average out the effects of all the other predictors in the model.

Constructing a PDP in practice is rather straightforward. To simplify, let $$\boldsymbol{z}_s = x_1$$ be the predictor variable of interest with unique values $$\left\{x_{11}, x_{12}, \dots, x_{1k}\right\}$$. The partial dependence of the response on $$x_1$$ can be constructed as follows:

• For $$i \in \left\{1, 2, \dots, k\right\}$$:

1. Copy the training data and replace the original values of $$x_1$$ with the constant $$x_{1i}$$.

2. Compute the vector of predicted values from the modified copy of the training data.

3. Compute the average prediction to obtain $$\bar{f}_1\left(x_{1i}\right)$$.

• Plot the pairs $$\left\{x_{1i}, \bar{f}_1\left(x_{1i}\right)\right\}$$ for $$i = 1, 2, \dotsc, k$$.

Algorithm 1 A simple algorithm for constructing the partial dependence of the response on a single predictor $$x_1$$

Algorithm 1 can be quite computationally intensive since it involves $$k$$ passes over the training records. Fortunately, the algorithm can be parallelized quite easily (see this vignette for details). It can also be easily extended to larger subsets of two or more features as well.

Limited implementations of Friedman’s PDPs are available in packages randomForest (Breiman et al. 2018) and gbm (others 2017), among others; these are limited in the sense that they only apply to the models fit using the respective package. For example, the partialPlot() function in randomForest only applies to objects of class "randomForest" and the plot() function in ghbm only applies to "gbm" objects. While the randomForest implementation will only allow for a single predictor, the gbm implementation can deal with any subset of the predictor space. Partial dependence functions are not restricted to tree-based models; they can be applied to any supervised learning algorithm (e.g., generalized additive models and neural networks). However, to our knowledge, there is no general package for constructing PDPs in R. For example, PDPs for a conditional random forest as implemented by the cforest() function in the party and partykit packages; see Hothorn et al. (2018) and Hothorn and Zeileis (2018), respectively. The pdp (Greenwell 2017) package tries to close this gap by offering a general framework for constructing PDPs that can be applied to several classes of fitted models.

The plotmo package (Milborrow 2018) is one alternative to pdp. According to Milborrow (2018), plotmo constructs “a poor man’s partial dependence plot.” In particular, it plots a model’s response when varying one or two predictors while holding the other predictors in the model constant (continuous features are fixed at their median value, while factors are held at their first level). These plots allow for up to two variables at a time. They are also less accurate than PDPs, but are faster to construct. For additive models (i.e., models with no interactions), these plots are identical in shape to PDPs. As of plotmo version 3.3.0, there is now support for constructing PDPs, but it is not the default. The main difference is that plotmo, rather than applying step 1. (a)-(c) in Algorithm 1, accumulates all the data at once thereby reducing the number of internal calls to predict(). The trade-off is a slight increase in speed at the expense of using more memory. So, why use the pdp package? As will be discussed in the upcoming sections, pdp:

• contains only a few functions with relatively few arguments;

• does NOT produce a plot by default;

• can be used more efficiently with "gbm" objects;

• produces graphics based on lattice (Sarkar 2017), which are more flexible than base R graphics;

• defaults to using false color level plots for multivariate displays;

• contains options to mitigate the risks associated with extrapolation;

• has the option to display progress bars;

• has the option to construct PDPs in parallel;

• is extremely flexible in the types of PDPs that can be produced.

PDPs can be misleading in the presence of substantial interactions (Goldstein et al. 2015). To overcome this issue Goldstein et al. (2015) developed the concept of individual conditional expectation (ICE) plots—available in the ICEbox package (Goldstein, Kapelner, and Bleich 2017). ICE plots display the estimated relationship between the response and a predictor of interest for each observation. Consequently, the PDP for a predictor of interest can be obtained by averaging the corresponding ICE curves across all observations. ICE curves can be obtained using the pdp package by setting ice = TRUE in the call to partial(). It is also possible to display the PDP for a single predictor with ICEbox; see ?ICEbox::plot.ice for an example. ICEbox only allows for one variable at a time (i.e., no multivariate displays), though color can be used effectively to display information about an additional predictor. The ability to construct centered ICE (c-ICE) plots and derivative ICE (d-ICE) plots is also available in ICEbox (same goes for pdp); c-ICE plots help visualize heterogeneity in the modeled relationship between observations, and d-ICE plots help to explore interaction effects.

Many other techniques exist for visualizing relationships between the predictors and the response based on a fitted model. For example, the car package (Fox and Weisberg 2011) contains many functions for constructing partial-residual and marginal-model plots. Effect displays, available in the effects package (Fox 2003), provide tabular and graphical displays for the terms in parametric models while holding all other predictors at some constant value—similar in spirit to plotmo’s marginal model plots. However, these methods were designed for simpler parametric models (e.g., linear and generalized linear models), whereas plotmo, ICEbox, and pdp are more useful for black box models (although, they can be used for simple parametric models as well).

## Constructing PDPs in R

The pdp package is useful for constructing PDPs for many classes of fitted models in R. PDPs are especially useful for visualizing the relationships discovered by complex machine learning algorithms such as a random forest. The three most important functions exported by pdp are:

• partial();

• plotPartial();

• autoplot().

The partial() function evaluates the partial dependence from a fitted model over a grid of predictor values; the fitted model and predictors are specified using the object and pred.var arguments, respectively—these are the only required arguments. If plot = FALSE (the default), partial() returns an object of class "partial" which inherits from the class "data.frame"; put another way, by default, partial() returns a data frame with an additional class that is recognized by the plotPartial() and autoplot() functions. The columns of the data frame are labeled in the same order as the features supplied to pred.var, and the last column is labeled yhat1 and contains the values of the partial dependence function $$\bar{f}_s\left(\boldsymbol{z}_s\right)$$. If plot = TRUE, then by default partial() makes an internal call to plotPartial() and returns the PDP in the form of a lattice plot (i.e., a "trellis" object). To use ggplot2 instead of lattice, set plot.engine = "ggplot2" in the call to partial(). Note: it is recommended to call partial() with plot = FALSE and store the results; this allows for more flexible plotting, and the user will not have to waste time calling partial() again if the default plot is not sufficient.

The plotPartial() and autoplot() functions can be used for displaying more advanced PDPs; they operate on objects of class "partial" and have many useful plotting options. For example, plotPartial() makes it straight forward to add a LOESS smooth, or produce a 3-D surface instead of a false color level plot (the default). (Note: the autoplot() function does not support the construction of 3-D surfaces.) 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.

Note: as mentioned above, pdp relies on lattice for its graphics. lattice itself is built on top of grid (R Core Team 2018). grid graphics behave a little differently than traditional R graphics, and two points are worth making (see ?lattice for more details):

• lattice and ggplot2 functions return "trellis" and "ggplot" objects, respectively, but do not display them; the print() method produces the actual displays. However, due to R’s automatic printing rule, the result is automatically printed when using these functions in the command line. If plotPartial() or autoplot() are called inside of source or inside a loop (e.g., for or while), an explicit print statement is required to display the resulting graph; hence, the same is true when using partial() with plot = TRUE.

• Setting graphical parameters via the par() function typically has no effect on lattice and ggplot2 graphics. For example, lattice provides its own trellis.par.set() function for modifying graphical parameters.

A consequence of the second point is that the par() function cannot be used to control the layout of multiple lattice (and hence pdp) plots. Simple solutions are available in packages latticeExtra (Sarkar and Andrews 2016) and gridExtra (Auguie 2017). For convenience, pdp imports the grid.arrange() function from gridExtra which makes it easy to display multiple grid-based graphical objects on a single plot (these include graphics produced using lattice and and ggplot2—hence, pdp). This is demonstrated in multiple examples throughout the vignettes in this package.

Currently supported models are described in Table 1 below. In these cases, the user does not need to supply a prediction function or a value for the type argument (i.e., "regression" or "classification"). In other situations, the user may need to specify one or both of these arguments. This allows partial() to be flexible enough to handle many of the model types not listed in Table 1; for example, neural networks from the nnet package (Venables and Ripley 2002) and projection pursuit regression (Friedman and Stuetzle 1981) using the ppr() function in the stats package.

Table 1 Models specifically supported by the pdp package. Note: for some of these cases, the user may still need to supply additional arguments in the call to partial().

Type of model R package Object class
Decision tree C50 (Kuhn and Quinlan 2018a) "C5.0"
party "BinaryTree"
partykit "party"
rpart (Therneau and Atkinson 2018) "rpart"
Bagged decision trees adabag (Alfaro et al. 2018) "bagging"
ipred (Peters and Hothorn 2017) "classbagg", "regbagg"
Boosted decision trees adabag (Alfaro et al. 2018) "boosting"
gbm "gbm"
xgboost "xgb.Booster"
Cubist Cubist (Kuhn and Quinlan 2018b) "cubist"
Discriminant analysis MASS (Venables and Ripley 2002) "lda", "qda"
Generalized linear model stats "glm", "lm"
Linear model stats "lm"
Nonlinear least squares stats "nls"
Multivariate adaptive regression splines (MARS) earth (Trevor Hastie and Thomas Lumley’s leaps wrapper. 2018) "earth"
Projection pursuit regression stats "ppr"
Random forest randomForest "randomForest"
ranger (Wright, Wager, and Probst 2018) "ranger"
party "RandomForest"
partykit "cforest"
Support vector machine e1071 (Meyer et al. 2018) "svm"
kernlab (Karatzoglou, Smola, and Hornik 2018) "ksvm"

The partial() function also supports objects of class "train" produced using the train() function from the well-known caret package (Jed Wing et al. 2018). This means that partial() can be used with any classification or regression model that has been fit using caret’s train() function; see http://topepo.github.io/caret/available-models.html for a current list of models supported by caret.

Another important argument to partial() is train. If train = NULL (the default), partial() tries to extract the original training data from the fitted model object. For objects that typically store a copy of the training data (e.g., objects of class "BinaryTree", "RandomForest", and "train"), this is straightforward. Otherwise, partial() will attempt to extract the call stored in object (if available) and use that to evaluate the training data in the same environment from which partial() was called. This can cause problems when, for example, the training data have been changed after fitting the model, but before calling partial(). Hence, it is good practice to always supply the training data via the train argument in the call to partial()2. If train = NULL and the training data can not be extracted from the fitted model, the user will be prompted with an informative error message (this will occur, for example, when using partial() with "ksvm" and "xgb.Booster" objects):

Error: The training data could not be extracted from object. Please supply
the raw training data using the train argument in the call to partial.

For illustration, we’ll use a corrected version of the Boston housing data analyzed in Harrison and Rubinfeld (1978); the data are available in the pdp package (see ?pdp::boston for details). We begin by loading the data and fitting a random forest with default tuning parameters and 500 trees:

# Load required packages
library(randomForest)  # for fitting random forests
library(pdp)           # for partial dependence plots
library(vip)           # for variable importance plots

# Fit a random forest to the Boston housing data
set.seed(101)  # for reproducibility
boston_rf <- randomForest(cmedv ~ ., data = boston, importance = TRUE)

# Variable importance plot (compare to randomForest::varImpPlot(boston_rf))
vip(boston_rf, bar = FALSE, horizontal = FALSE, size = 1.5)  # Figure 1

The model fit is reasonable, with an out-of-bag (pseudo) $$R^2$$ of 0.89. The variable importance scores are displayed in Figure 1. Both plots indicate that the percentage of lower status of the population (lstat) and the average number of rooms per dwelling (rm) are highly associated with the median value of owner-occupied homes (cmedv). The question then arises, “What is the nature of these associations?” To help answer this, we can look at the partial dependence of cmedv on lstat and rm, both individually and together.

### Single predictor PDPs

As previously mentioned, the randomForest package has its own partialPlot() function for visualizing the partial dependence of the response on a single predictor—the keywords here are “single predictor”. For example, the following snippet of code plots the partial dependence of cmedv on lstat (the result is displayed in Figure 2):

partialPlot(boston_rf, pred.data = boston, x.var = "lstat")  # Figure 2

The same plot can be achieved using the partial() function and setting plot = TRUE (see Figure 3). The only difference is that pdp uses the lattice and ggplot2 graphics packages to produce all of its displays.

# Load required packages
library(ggplot2)
library(pdp)

# Default lattice-based PDP
p1 <- partial(boston_rf, pred.var = "lstat", plot = TRUE, rug = TRUE)

# Switch to ggplot2
p2 <- partial(boston_rf, pred.var = "lstat", plot = TRUE,
plot.engine = "ggplot2")

# Figure 3
grid.arrange(p1, p2, ncol = 2)  

For a more customizable plot, we can set plot = FALSE in the call to partial() and then use the plotPartial() and autoplot() functions on the resulting data frame. This is illustrated in the example below which increases the line width, adds a LOESS smooth, and customizes the $$y$$-axis label. The result is displayed in Figure 4. Note: to encourage writing more readable code, the forward pipe operator \%>\% provided by the magrittr package (Bache and Wickham 2014) is exported whenever pdp is loaded.

# lattice-based PDP
p1 <- boston_rf %>%  # the %>% operator is read as "and then"
partial(pred.var = "lstat") %>%
plotPartial(smooth = TRUE, lwd = 2, ylab = expression(f(lstat)),
main = "lattice-based PDP")

# ggplot2-based PDP
p2 <- boston_rf %>%  # the %>% operator is read as "and then"
partial(pred.var = "lstat") %>%
autoplot(smooth = TRUE, ylab = expression(f(lstat))) +
theme_light() +
ggtitle("ggplot2-based PDP")

# Figure 4
grid.arrange(p1, p2, ncol = 2)  

## Multi-predictor PDPs

The benefit of using partial is threefold: (1) it is a flexible, generic function that can be used to obtain different kinds of PDPs for various types of fitted models (not just random forests), (2) it will allow for any number of predictors to be used (e.g., multivariate displays), and (3) it can utilize any of the parallel backends supported by the foreach package (Microsoft and Weston 2017); we discuss parallel execution in a later section. For example, the following code chunk uses the random forest model to assess the joint effect of lstat and rm on cmedv. The grid.arrange() function is used to display three PDPs, which make use of various plotPartial options3, on the same graph. The results are displayed in Figure 5.

# Compute partial dependence data for lstat and rm
pd <- partial(boston_rf, pred.var = c("lstat", "rm"))

# Default PDP
pdp1 <- plotPartial(pd)

# Add contour lines and use a different color palette
rwb <- colorRampPalette(c("red", "white", "blue"))
pdp2 <- plotPartial(pd, contour = TRUE, col.regions = rwb)

# 3-D surface
pdp3 <- plotPartial(pd, levelplot = FALSE, zlab = "cmedv", colorkey = TRUE,
screen = list(z = -20, x = -60))

# Figure 5
grid.arrange(pdp1, pdp2, pdp3, ncol = 3)

Note that the default color map for level plots is the color blind-friendly matplotlib (Hunter 2007) ‘viridis’ color map provided by the viridis package (Garnier 2018).

## Avoiding extrapolation

It is not wise to draw conclusions from PDPs in regions outside the area of the training data. Here we describe two ways to mitigate the risk of extrapolation in PDPs: rug displays and convex hulls. Rug displays are one-dimensional plots added to the axes. The partial(), plotPartial(), and autoplot() functions all have a rug option that, when set to TRUE, will display the deciles of the distribution (as well as the minimum and maximum values) for the predictors on the horizontal and vertical axes. Using the deciles is especially useful when constructing PDPs from large training data sets (where using a tick mark for each unique observation will just end up looking like a solid strip). The following snippet of code produces Figure 6.

# Figure 6
partial(boston_rf, pred.var = "lstat", plot = TRUE, rug = TRUE,
plot.engine = "ggplot2")

In two or more dimensions, plotting the convex hull is more informative; it outlines the region of the predictor space that the model was trained on. When , the convex hull of the first two dimensions of $$\boldsymbol{z}_s$$ (i.e., the first two variables supplied to pred.var) is computed; for example, if you set chull = TRUE in the call to partial() only the region within the convex hull of the first two variables is plotted. Over interpreting the PDP outside of this region is considered extrapolation and is ill-advised. The right display in Figure 7 was produced using:

p1 <- partial(boston_rf, pred.var = c("lstat", "rm"), plot = TRUE, chull = TRUE)
p2 <- partial(boston_rf, pred.var = c("lstat", "rm"), plot = TRUE, chull = TRUE,
palette = "magma")
grid.arrange(p1, p2, nrow = 1)  # Figure 7

## Other vignettes

The following vignettes offer additional details on using the pdp package:

• Coming soon!

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1. There is one exception to this. When a function supplied via the pred.fun argument returns multiple predictions, the second to last and last columns will be labeled yhat and yhat.id, respectively.

2. For brevity, we ignore this option in most of the examples in this vignette.

3. See this vignette for an example of how to add a label to the colorkey in these types of graphs.