Citation:Haghish, E. F. (2023). shapley: Weighted Mean SHAP for Feature Selection in ML Grid and Ensemble [computer software]. URL: https://CRAN.R-project.org/package=shapley

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`shapley`

The `shapley`

R package addresses a significant limitation
in machine learning research by providing a method to calculate the
weighted mean and confidence intervals of **SHapley Additive
exPlanations**, commonly known as **SHAP values**
across machine learning grids and stacked ensemble models. This approach
enhances the stability and reliability of SHAP values, making the
determination of important features more transparent and potentially
more reproducible. Traditionally, the focus has been on reporting SHAP
values from a single ‘best’ model, which can be problematic under
conditions of severe class imbalance, where a universally accepted
‘best’ model may not exist. In addition, models with different
parameters, might result in different evaluations of SHAP contributions
and such variablity is also meaningful for researchers who wish to
understand important features relevant to a model. In other words, SHAP
values are unstable and varry across models, a limitation that is often
overlooked in the literature by reporting the SHAP contributions of the
‘best’ model. In such scenarios, the SHAP values from a single model may
not be representative of other models. The `shapley`

package
fills a critical gap by proposing methodology and enabling the
computation of SHAP values for multiple machine learning models such as
a fine-tuning grid search, and stacked ensemble models. This method
computes weighted mean SHAP contributions, considering the performance
of the model, to compute more stable SHAP values that also reflect of
the variations across models.

In particular, the `shapley`

software addresses the
following shortcomings, often found in recent literature of applied
machine learning:

The instability of SHAP values reported from single models is a concern, especially when the model’s performance and the variability of SHAP values across multiple fine-tuned models are not taken into account. With the increase in severity of class imbalance, the instability is expected to increase, because the definition of “best model” becomes less reliable due to lack of global performance metric that is not biased to class imbalance.

There is an absence of standardized methods for calculating SHAP values for stacked ensemble models or for determining the SHAP contributions of features within the entirety of models resulting from a tuned grid search.

There is a notable gap in methods for computing confidence intervals for SHAP values. Such intervals are necessary for significance testing to determine if one feature’s importance is statistically greater than that of another. They also would reflect the variability between how different models reflect on SHAP values of different features.

The practice of identifying “top features” often relies on an arbitrary selection of a predefined number of features, such as the top 10 or top 20, without a standard methodological approach. There is a need for a more transparent and automated procedure for quantifying and identifying important features in a model.

`shapley`

R packageThe `shapley`

R package computes the weighted average and
confidence intervals of Shapley values from multiple machine learning
models. By incorporating model performance metrics as weights, it
addresses the variability in SHAP values across different models, which
is often overlooked when relying on a single “best” model. This approach
is particularly valuable in situations where defining the best model is
challenging, such as with severe class imbalances (class rarity, caused
by low-prevalence outcome). The package also facilitates more reliable
computation of SHAP contributions across models and provides a basis for
significance testing between features to ensure differences are not due
to random chance. Furthermore, the package proposes several automated
and transparent methods for identifying important features. These
methods use various metrics to define importance, allowing for the
selection of significant features based on SHAP contributions without
pre-specifying number of top features (see below for details).

To demonstrate how ** shapley** can compute
SHAP values across a machine learning grid, let’s carry out a grid
search to fine-tune Gradient Boosting Machines (GBM) algorithm for a
binary classification. Next, I will use the grid to compute SHAP
contributions across all models and report their weighted mean and
weighted 95% confidence intervals.

```
library(h2o) #shapley supports h2o models
library(shapley)
# initiate the h2o server
h2o.init(ignore_config = TRUE, nthreads = 2, bind_to_localhost = FALSE, insecure = TRUE)
# upload data to h2o cloud
<- system.file("extdata", "prostate.csv", package = "h2o")
prostate_path <- h2o.importFile(path = prostate_path, header = TRUE)
prostate
# run AutoML to tune various models (GBM) for 60 seconds
<- "CAPSULE"
y <- as.factor(prostate[,y]) #convert to factor for classification
prostate[,y]
set.seed(10)
#######################################################
### PREPARE H2O Grid (takes a couple of minutes)
#######################################################
# make sure equal number of "nfolds" is specified for different grids
<- h2o.grid(algorithm = "gbm", y = y, training_frame = prostate,
grid hyper_params = list(ntrees = seq(1,50,1)),
grid_id = "ensemble_grid",
# this setting ensures the models are comparable for building a meta learner
seed = 2023, fold_assignment = "Modulo", nfolds = 10,
keep_cross_validation_predictions = TRUE)
<- shapley(grid, newdata = prostate, performance_metric = "aucpr", plot = TRUE) result
```

In the example above, the `result`

object would be a
*list of class shapley*, which in cludes the
information such as weighted mean and weighted confidence intervals as
well as other metrics regarding SHAP contributions of different
features.

You can use the ** shapley.plot** function
to plot the SHAP contributions:

- To plot weighted mean SHAP contributions as well as weighted 95%
confidence intervals, pass the
`shapley object`

, in this example, named`result`

, and specify`"bar"`

, to create a bar plot:

`shapley.plot(result, plot = "bar")`

Another type of plot, that is also useful for identifying important
features is ** waffle** plot, by default
showing any feature that at least has contributed 0.25% to the overall
explained SHAP values across features.

`shapley.plot(result, plot = "waffle")`

Another type of plot is ** shap** plot,
which shows the SHAP contributions of each feature for each observation
(subject, or row in the data). This plot is useful for identifying the
direction of the effect of each feature on the outcome, improving the
transparency of the model. What is noteworthy about the

`shap`

`shapley.plot(result, plot="shap")`

Note: the weighted mean SHAP contribution plot of observations is expected to more clearly differentiate between how different values of a feature affect the outcome.

- Weighted mean SHAP contributions of all models from the tuning grid

For instance, in the plot above, the effect of “GLEASON” feature on the outcome is more clearly differentiated between different values of the feature, compared to the plot of SHAP contributions of a

the bestmodel, as shown below. As you see, subjects with very high SHAP values that are shown in thebest modelbelow are not present in the plot of weighted mean SHAP contributions, meaning that different models did not agree on the effect of “GLEASON” feature on the outcome and thus, the voice of different models is taken into account, weighted by their performance metric. It is also evident that the SHAP contributions of the weighted mean SHAP model are more clearly demonstrate the relationship of the feature on the outcome. See for example, the ‘DPROS’ feature, where the SHAP values are somehow well-clustered in the weighted mean SHAP plot, indicating that collectively, the models clearly see a pattern between increased intensity of ‘DPROS’ with the outcome.

- SHAP contributions of the best model

Weighted mean SHAP values and their confidence intervals can also be
computed for **Factors** (A group of items) or
**Domains** (A group of correlated or related factors
presenting a domain). For both, the `domain`

argument should
be specified so that the software compute the contribution of a cluster
of items. There is no difference between *Factors* and
*Domains*, because for either, they need to be defined as a group
of features (variables / columns) in the dataset.

```
print(shapley.plot(shapley = result,
plot = "bar", method = "mean",
domains = list(Demographic = c("RACE", "AGE"),
Cancer = c("VOL", "PSA", "GLEASON"),
Tests = c("DPROS", "DCAPS")),
print = TRUE))
```

The analysis results are also returned in a Table:

```
feature mean sd ci lowerCI upperCI
1 Demographic 0.09730516 0.01304978 0.004669805 0.09263535 0.1019750
2 Cancer 0.70783878 0.01747264 0.006252502 0.70158628 0.7140913
3 Tests 0.19485606 0.01269251 0.004541957 0.19031411 0.1993980
```

You can also compute weighted mean SHAP values for each subject (row)
in the dataset. To do that, use the `shapley.row.plot`

function. For example, to view the SHAP contributions and their
confidence intervals for the 20th row in the dataset, type:

`shapley.row.plot(result, row_index = 20)`

The bar plot displays the weighted average and confidence intervals
for the SHAP contributions of various features, implying whether
differences between two features might be due to random variation. To
further investigate the statistical significance of these differences,
the `shapley.test`

function can be employed. This function
uses a permutation test, typically with a default of 5000 permutations,
to assess significance. For instance, to determine if the observed
difference in SHAP contributions between the “GLEASON” and “DPROS”
features is not just a random occurrence, we would use this
function.

`shapley.test(result, features = c("GLEASON", "DPROS"), n = 5000)`

```
The difference between the two features is significant:
observed weighted mean Shapley difference = 150.014579392817 and p-value = 0
$mean_shapley_diff
[1] 150.0146
$p_value
[1] 0
```

However, if we check the difference between “PSA” and “ID” features, the difference is insignificant, although “PSA” has a slightly higher weighted mean SHAP contribution:

`shapley.test(result, features = c("PSA", "ID"), n = 5000)`

```
The difference between the two features is not significant:
observed weighted mean Shapley difference =2.44791199071206 and p-value = 0.678
$mean_shapley_diff
[1] 2.447912
$p_value
[1] 0.678
```

Note: the weighted confidence intervals showed in the bar plot do not apply any permutation test.

Traditionally, the selection of a set number of significant features
based on the highest SHAP values varied across scientific publications,
with some reporting the top 10, 15, or 20. This selection did not
account for the variability between models and often was either an
arbitrary number. Other papers reported feature importance of all
features, which is not practical for large datasets, and also, doesn’t
consider if features with neglegible SHAP contributions are
statistically significant, given the variability across models. However,
the calculation of weighted means and 95% confidence intervals allows
for a systematic approach to identify features that consistently
contribute to the model, across different models. For instance, the
default method in the `shapley`

package considers features
important if their lower bound of the weighted 95% confidence interval
(“`lowerCI`

”) for relative mean SHAP value exceeds
`0.01`

. This means any feature with a stable relative
contribution of at least 1% - relative to the feature with the highest
SHAP - is deemed important. This method is also utilized in the
`bar`

plot, where features are ranked by their weighted mean
SHAP values, and the cutoff is applied to the lower confidence interval.
This threshold can be adjusted to “`mean`

”, which sets the
cutoff for weighted means, disregarding model variability—although this
is experimental and might not be best practice, because it overlooks the
variablitity across models and only considers the weighted means.
Another experimental criterion - that seems more plausible than the
“`mean`

” strategy is “`shapratio`

”, setting a
cutoff for the minimum weighted mean SHAP value as a percentage of the
total SHAP contributions from all features. This is demonstrated in the
`waffle`

plot, where features must contribute at least 0.5%
to the overall weighted mean SHAP values to be selected.

Between the “`lowerCI`

” and “`shapratio`

”
methods, each has merits and limitations. “`LowerCI`

”
addresses variability across models, while “`shapratio`

”
focuses on feature contribution variability. Implementing both could
provide insight into their efficacy and alignment in practice. Future
research should explore these methodologies further.

In this regard, the package also suggests a function for testing
different criteria. Currently, implementing only `lowerci`

and `shapratio`

criteria. following the examples above, the
** shapley.top** shows features that pass
different criteria:

```
# running shapley.top with defult values for the 'result' object
shapley.top(result, lowerci = 0.01, shapratio = 0.005)
```

```
feature lowerci shapratio lowerCI_criteria shapratio_criteria
1 AGE 0.13241618 0.056009832 TRUE TRUE
2 DCAPS 0.09566612 0.041606347 TRUE TRUE
3 DPROS 0.39986992 0.164283698 TRUE TRUE
4 GLEASON 0.89025073 0.364372706 TRUE TRUE
5 ID 0.33566464 0.141787423 TRUE TRUE
6 PSA 0.34900145 0.145052440 TRUE TRUE
8 VOL 0.19571663 0.082300782 TRUE TRUE
7 RACE 0.01079821 0.004586772 TRUE FALSE
```

which shows the features that pass both or one of the criteria. There is, however, a need for further research to suggest optimal cutoff values or reach a better perspective that what aspects should be taken into consideration for suggesting optimal cutoff values.

Stacked ensemble models integrate multiple base learner models’
predictions, assigning weights according to each base model’s
performance. The methodology implemented in `shapley`

software employs a similar approach, calculating the weighted mean SHAP
contributions for stacked ensemble models just as it would for a
fine-tuned grid of models. The `shapley`

function’s
`models`

parameter automatically detects whether the input is
an `h2o`

grid or an `h2o`

or
`autoEnsemble`

stacked ensemble, eliminating the need for
users to identify the model object class.

The package is compatible with machine learning grids or stacked
ensemble models created using the `h2o`

package, as well as the `autoEnsemble`

package in R.

The methodology implemented in this software as well as the software itself was developed by E.F. Haghish, who is a researcher at Department of Psychology, University of Oslo, researching applications of machine learning for mental health.

Twitter: @haghish