permimp-package is developed to replace the Conditional Permutation Importance (CPI) computation by the
varimp-function(s) of the
permimpapplies a different implementation for the CPI, in order to mitigate some issues related to the implementation of the CPI in the
party-package. In addition, the CPI is also available for random forests grown by the
randomForest-package. Finally, the package includes some plotting options.
Although originally designed for prediction purposes, Random forests Breiman (2001) have become a popular tool to assess the importance of predictors. Several methods and measures have been proposed, one of the most popular ones is the Permutation Importance Breiman (2001), originally referred to as the Mean Decrease in Accuracy. Inspired by the contrast between the unconditional zero-order correlation between predictor and outcome, and the conditional standardized regression coefficient in multiple linear regression, Strobl et al. (2008) argued that in some cases the importance of a predictor, conditionally on (all) other predictors, may be of higher interest than the unconditional importance. Therefore, they proposed the Conditional Permutation Importance, which introduces a conditional permutation scheme that is based on the dependence between the predictors.
permimp-package presents a different implementation of this Conditional Permutation Importance. Unlike the original implementation (available in the
party R-package of Hothorn, Hornik, and Zeileis (2006)),
permimp can, in addition to random forests that were grown according to the unbiased recursive partitioning (cf.
cforests; Hothorn, Hornik, and Zeileis (2006)), also deal with with random forests that were grown using the
randomForest-package Liaw and Wiener (2002), which applies the original tree growing algorithm based on impurity reduction Breiman (2001). (In principle, the
permimp can be extended to random forests grown by other packages, under the condition that tree-wise predictions are possible and OOB-information as well as the split points are available per tree.) We argue that the
permimp-package can be seen as a replacement for the
varimp-functions of the
party package in R.
This vignette has two main parts. The first part is tutorial-like and demonstrates functionality of the
permimp-package (by also comparing it to original
party::varimp-functions. The second part is more theoretical and explains the how and the why of the new Conditional Permutation Importance-implementation.
permimp-function replaces all the
varimpsurv). To apply
permimp-function, one needs a fitted random forest. Within this tutorial we will mainly focus on random forests-objects as obtained by the
party::cforest-function (i.e., S4-objects of class
"RandomForest"). As an example we will use the (cleaned)
airquality-data set to fit random forest with 50 trees:
library("party", quietly = TRUE) #> #> Attaching package: 'zoo' #> The following objects are masked from 'package:base': #> #> as.Date, as.Date.numeric library("permimp") set.seed(542863) airq <- subset(airquality, !(is.na(Ozone) | is.na(Solar.R))) cfAirq50 <- cforest(Ozone ~ ., data = airq, control = cforest_unbiased(mtry = 2, ntree = 50, minbucket = 5, minsplit = 10))
Let’s start by comparing the
permimp and the
varimp function for the conditional permutation importance.
system.time(CPI_permimp <- permimp(cfAirq50, conditional = TRUE, progressBar = FALSE)) #> user system elapsed #> 0.32 0.02 0.34 system.time(CPI_varimp <- varimp(cfAirq50, conditional = TRUE)) #> user system elapsed #> 2.31 0.02 2.34 CPI_permimp #> Solar.R Wind Temp Month Day #> 83.736656 209.786231 422.671385 1.820496 -7.668462 CPI_varimp #> Solar.R Wind Temp Month Day #> 25.147792 114.250197 220.080351 1.952776 -1.265111
Three differences can easily be spotted:
progressBar-argument. The default is
progressBar = TRUE1
permimpis faster than
Why are the results different?
There are two main reasons. First,
permimp uses a different default
threshold = .95 while
threshold = 0.2. Check
?varimp. There is a good reason for using a higher default threshold value.
When using equal
The results are more similar, but not quite identical. The remaining differences are explained by the second reason: the implementation of
permimp differs from the
varimp-implementation. Using a higher
threshold-value makes the differences between the two implementations more pronounced.
The differences between the two implementations (and why we believe the new implementation is more attractive), is explained in the second part of this document, as well as in this manuscript: Debeer and Strobl (2020).
asParty = TRUE
asParty = TRUE, the
permimp-function can be made backward compatible with the
permimp is a bit faster. To get exactly the same results, the random seeds should be exactly the same.
set.seed(542863) system.time(CPI_asParty <- permimp(cfAirq50, conditional = TRUE, asParty = TRUE, progressBar = FALSE)) #> user system elapsed #> 0.45 0.00 0.46 set.seed(542863) system.time(CPI_varimp <- varimp(cfAirq50, conditional = TRUE)) #> user system elapsed #> 2.23 0.00 2.24 CPI_asParty #> Solar.R Wind Temp Month Day #> 36.364271 136.732886 200.620728 3.179600 1.360632 CPI_varimp #> Solar.R Wind Temp Month Day #> 36.364271 136.732886 200.620728 3.179600 1.360632
Note that with
asParty = TRUE the default
threshold-value is automatically set back to
A less obvious difference between
varimp is the object that it returns.
permimp returns an S3-class object:
VarImp, rather than a named numerical vector. A
VarImp object is a named list with four elements:
$values: holds the computed variable importance values.
$perTree: holds the variable importance values per tree (averaged over the permutations when
nperm > 1).
$type: the type of variable importance.
$info: other relevant information about the variable importance, such as the used
## varimp returns a named numerical vector. str(CPI_varimp) #> Named num [1:5] 36.36 136.73 200.62 3.18 1.36 #> - attr(*, "names")= chr [1:5] "Solar.R" "Wind" "Temp" "Month" ... ## permimp returns a VarImp-object. str(CPI_asParty) #> List of 4 #> $ values : Named num [1:5] 36.36 136.73 200.62 3.18 1.36 #> ..- attr(*, "names")= chr [1:5] "Solar.R" "Wind" "Temp" "Month" ... #> $ perTree:'data.frame': 50 obs. of 5 variables: #> ..$ Solar.R: num [1:50] 117.35 0 1.81 0 58.22 ... #> ..$ Wind : num [1:50] 118.8 430.7 141.5 171 78.1 ... #> ..$ Temp : num [1:50] 374 433 118 -1 175 ... #> ..$ Month : num [1:50] -4.59 0 34.93 -11.96 -11.9 ... #> ..$ Day : num [1:50] 0 18.93 0 7.18 0 ... #> $ type : chr "Conditional Permutation" #> $ info :List of 4 #> ..$ threshold : num 0.2 #> ..$ conditioning: chr "as party" #> ..$ outcomeType : chr "regression" #> ..$ errorType : chr "MSE" #> - attr(*, "class")= chr "VarImp" ## the results of permimp(asParty = TRUE) and varimp() are exactly the same. all(CPI_asParty$values == CPI_varimp) #>  TRUE
An advantage of the
VarImp-object, is that the
$perTree-values can be used to inspect the distribution of the importance values across the trees in a forest. For instance, the plotting function (demonstrated below) can be used to visualize this distribution of per tree importance values.
Of course, there is also the option to compute the unconditional permutation importance. Both using the original and the split wise permutation algorithm. Here, there are no differences between
varimp. That is,
permimp simply uses the
varimp code, making the
asParty argument redundant in this case. Note, however, that
permimp still returns a
## Original Unconditional Permutation Importance set.seed(542863) PI_permimp <- permimp(cfAirq50, progressBar = FALSE, pre1.0_0 = TRUE) set.seed(542863) PI_varimp <- varimp(cfAirq50, pre1.0_0 = TRUE) PI_permimp #> Solar.R Wind Temp Month Day #> 104.19612764 345.36320352 582.09815801 18.04859049 0.01880503 PI_varimp #> Solar.R Wind Temp Month Day #> 104.19612764 345.36320352 582.09815801 18.04859049 0.01880503 ## Splitwise Unconditional Permutation Importance set.seed(542863) PI_permimp2 <- permimp(cfAirq50, progressBar = FALSE) set.seed(542863) PI_varimp2 <- varimp(cfAirq50) PI_permimp2 #> Solar.R Wind Temp Month Day #> 81.935250 451.459770 580.918085 21.851431 -4.613963 PI_varimp2 #> Solar.R Wind Temp Month Day #> 81.935250 451.459770 580.918085 21.851431 -4.613963
For more detailed information check
Visualizing the variable importance values (as a
VarImp-object) is easy using the
plot method. Its main features include:
sort = FALSErenders the original order (cf. the
horizontal = TRUEhorizontal plots are made.
$perTreeimportance value distribution with the
type = "box", the distribution of the
$perTree-values is automatically visualized.2
We would suggest to only use the visualization of the
$perTree importance value distribution, when there are enough trees (>= 500) in the random forest. Therefore, we first fit a new, bigger random forest, and compute the permutation importance.
## fit a new forest with 500 trees set.seed(542863) cfAirq500 <- cforest(Ozone ~ ., data = airq, control = cforest_unbiased(mtry = 2, ntree = 500, minbucket = 5, minsplit = 10)) ## compute permutation importance PI_permimp500 <- permimp(cfAirq500, progressBar = FALSE) ## different plots, all easy to make ## barplot plot(PI_permimp500, type = "bar")
intervalProbs = c(<lower_quantile>, <upper_quantile>).3
<integer value>predictors with the highest values with
nVar = <integer value>.
interval = "sd". This is almost always a very bad idea, because it falsely suggests that the distribution is symmetric. Please don’t use this option.
For more detailed information check
(Currently) there are three more
ranks: prints the (reverse) rankings of the
subset: creates a subset that is itself also a
VarImp-object. Only to be used in very limited settings, and when you know what you are doing.
Other related functions are:
as.VarImp: creates a
VarImp-object from a
perTreevalues, or from a numerical vector of importance values.
is.VarImp: checks if an object is of the
As mentioned in the introduction, the
permimp-package can also deal with with random forests that were grown using the
randomForest-package Liaw and Wiener (2002), which applies the original tree growing algorithm based on impurity reduction Breiman (2001).
Let’s first grow a (small) forest.
keep.forest = TRUE and
keep.inbag = TRUE. The
permimp-function requires information about which observations were in-bag (IB) or out-of-bag (OOB), as well as information about the split points in each tree. Without this information, the (Conditional) Permutation Importance algorithm cannot be executed.
permimp for a
randomForest object form the
randomForest-package, a menu is prompted that ask whether you are sure that the data-objects used to fit the random forest have not changed. This is because the
permimp computations rely on those data-objects, and automatically search for them in the environment. If these data-objects have changed, the
permimp results can be distorted.
This part explains the new implementation of the conditional permutation importance, and discusses the differences with the original implementation in
party, as described by Strobl et al. (2008). First the the idea behind the conditional implementation is briefly recapitulated, followed by a discussion of the original implementation. Then the new implementation is explained, and the main differences with the original are emphasized. Finally, some practical implications of the new implementation are given, and the interpretation and possible use of the
threshold value are discussed.
A researcher may be interested in whether a predictor \(X\) and the outcome \(Y\) are independent. The “null-hypothesis” is then \(P(Y | X) = P(Y)\). This corresponds with the unconditional permutation importance. When \(X\) and \(Y\) are indeed independent, permuting \(X\) should not significantly change the prediction accuracy of the tree/forest. The expected permutation importance value is zero.
However, a researcher may also be interested in the conditional independence of \(X\) and \(Y\), conditionally on the values of some other predictors \(Z\). The “null-hypothesis” is then \(P(Y | X, Z) = P(Y | Z)\). Rather than “completely” permuting the \(X\) values, the \(X\) values can be permuted conditionally, given their corresponding \(Z\) values. This corresponds to the conditional permutation scheme. When \(X\) and \(Y\) are conditionally independent, ideally, a conditional importance measure should be zero.
If \(X\) and \(Z\) are independent, both permutation schemes will give the same results. Or in practice, similar importance values. Yet a dependence between \(X\) and \(Z\) will result in differences between the unconditional and the conditional permutation schemes, and the corresponding importance values.
Strobl et al. (2008) proposed to specify a partitioning (grid) of the predictor space based on \(Z\) (for each tree), in order to (conditionally) permute the values of \(X\) withing each partition (i.e., cell in the grid). According to Strobl et al. (2008) this partitioning should (1) be applicable to variables of all types; (2) be as parsimonious as possible, but (3) be also computationally feasible. Therefore they suggested to define the partitioning grid for each tree by means of the partitions of the predictor space induced by that tree. More precisely, using all the split points for \(Z\) in the tree, \(Z\) is discretized and the complete predictor space is partitioned using the discretized \(Z\).
Note that this partitioning does not correspond with the recursive partitioning of a tree. In a tree only the top node splits the complete predictor space, all the following splits are conditional on the parent nodes. In contrast, for the conditional permutation grid, all the split points split the complete predictor space, which leads to a more fine-grained grid.
In practice, the number of observations is finite. In situation with a relatively low number of observations, the grid for the conditional permutation may become to fine grained, making conditionally permuting practically infeasible. Therefore, the selection of \(Z\) (the predictors to condition on) is not a sinecure.
In their original implementation (cf,
party::varimp), Strobl et al. (2008) argued to only include those variables in \(Z\) whose empirical correlation with \(X\) exceeds a certain moderate threshold. For continuous variables the Pearson correlation could be used, but for the general case they proposed to use the conditional inference framework promoted by Hothorn, Hornik, and Zeileis (2006). Applying this framework provides p-values, which have the advantage that they are comparable for variables of all types, and that they can serve as an intuitive and objective means of selecting the variables Z to condition on.
The original implementation can be described as follows:
For every predictor \(X\)
- Test which other predictors are related to \(X\), applying the conditional inference framework (Hothorn et al. 2006) using the full data/training set.
- Only include those other predictors in \(Z\) for which the \(p\)-value of the test is smaller than
(1 - threshold).
- Within each tree:
- Gather all the split points for every predictor in \(Z\).
- Discretize the predictors in \(Z\) using the gathered split points, and create a partitioning of the predictor space.
- Within each partition, permute the values of predictor \(X\).
There are, however, two important issues with this implementation:
The new implementation tries to mitigate the two issues raised above, by taking advantage of the fact that within each tree not the original values of the predictors, but only the partitions are important for the prediction of the outcome. That is, one can argue that the tree-based partitioning rather than the original values should be used to decide which other predictors should be included in \(Z\). Applying this rationale, the new implementation can be described as follows:
In every tree, for every predictor with splits in the tree:
Discretize the in-bag values for each predictor using the split points: \(X\) => \(X_d\).
For every discretized \(X_d\):
- Test which other discretized predictors \(W_d\) are related to \(X_d\), applying a \(\chi^2\)-independence tests (using only the in-bag values).
- Only include those other predictors \(W\) in \(Z\) for which the \(p\)-value of the test is smaller than
(1 - threshold).
- Create the partitioning of the predictor space using the discretized \(Z\).
- Within each partition, permute the values of predictor \(X\).
The \(\chi^2\)-independence test does not (directly) depend on sample size. Therefore, the new implementation is less sensitive to the number of observations. In addition, the \(\chi^2\)-independence test is not limited to linear dependence. Hence, the new implementation mitigates the two issue raised above. Because of this, the
threshold-value is easier to use and interpret (see below).
Under the new implementation it is possible that \(Z\) differs across trees. Yet this is also the case under the original implementation, since not all predictors in \(Z\) are used as splitting variable in each tree. In addition, due to the randomness in random forests (subsampling/bootstrapping and
mtry selection), it is very unlikely that there are two trees in the forest with exactly the same splitting points. Therefore, the conditional sampling scheme almost surely differs across trees.
threshold-value can be interpreted as a tuning parameter to make the permutation more or less conditional. A
threshold = 0 and a
threshold = 1 corresponding to permuting as conditional as possible and permuting completely unconditional, respectively. A
threshold = .95, the default in
permimp, only includes those \(W\) in \(Z\) for which \(W_d\) and \(X_d\) are dependent (with \(\alpha\)-level = .05).
Yet threshold values smaller than
threshold = .5 generally make the selection of the predictors to condition on too greedy, without a meaningful impact on the CPI pattern. Therefore, we recommend using threshold values between .5 and 1.
Some research questions are best answered with a more marginal importance measure, while other questions are better answered using a more partial importance measure. In many situations, however, it is not clear which measure best fits the research question. Therefore, we argue that in these cases it can be interesting to evaluate the importance (rankings) of the predictors for different
threshold-values. This strategy can provide more insight in how the conditioning affects the permutation importance values.
In the original implementation, setting a sensible
threshold proved to be hard, because the practical meaning of the
threshold depended on the sample size and on the type of variables (cf. the issues raised above). In the new implementation, the
threshold’s interpretation is clearer and more stable. In addition, the simulation studies by Debeer and Strobl (2020) suggest that the new implementation (a) allows a more gradual shift from unconditional to conditional; and (b) gives more stable importance measure computations.
As an additional feature, the
permimp can provide some diagnostics about the conditional permutation. When
thresholdDiagnostics = TRUE, the
permimp-function monitors whether or not a conditional permutation scheme was feasible for each predictor \(X\) in each tree. This information is translated in messages that suggest to either or decrease the
First, it is possible that the conditioning grid is so fine-grained that permuting \(X\) conditionally cannot lead to observations ending up in a different end-node of the tree. In other words, the prediction accuracy before and after permuting will be always equal. If this issue occurs in more than 50 percent of the trees that include \(X\) as a splitting variable,
permimp will produce a note, and suggest to increase the
threshold-value. A higher
threshold-value may result in a less fine-grained partitioning, making the conditional permutation feasible again.
Second, it is possible that there are no \(W\) in the tree for which the \(\chi^2\)-independence test between \(W_d\) and \(X_d\) is smaller than
(1 - threshold). This implies \(Z\) will be an empty set, and conditionally permuting is impossible. That is, without a partitioning/grid, it is equal to unconditionally permuting. If this issue occurs in more than 50 percent of the trees that include \(X\) as a splitting variable,
permimp will produce a note, and suggest to decrease the
threshold-value. A lower
threshold-value includes more \(W\) in \(Z\), making the conditional permutation feasible again.
Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/a:1010933404324.
Debeer, Dries, and Carolin Strobl. 2020. “Conditional Permuation Importance Revisited.”
Hothorn, Torsten, Kurt Hornik, Mark A van de Wiel, and Achim Zeileis. 2006. “A Lego System for Conditional Inference.” The American Statistician 60 (August). https://doi.org/https://doi.org/10.1198/000313006X118430.
Hothorn, Torsten, Kurt Hornik, and Achim Zeileis. 2006. “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics 15 (3): 651–74. https://doi.org/10.1198/106186006x133933.
Liaw, Andy, and Matthew Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22. https://CRAN.R-project.org/doc/Rnews/.
Strobl, Carolin, Anne-Laure Boulesteix, Thomas Kneib, Thomas Augustin, and Achim Zeileis. 2008. “Conditional Variable Importance for Random Forests.” BMC Bioinformatics 9 (1): 307. https://doi.org/10.1186/1471-2105-9-307.