Shapley computes feature contributions for single predictions with the Shapley value, an approach from cooperative game theory. The features values of an instance cooperate to achieve the prediction. The Shapley value fairly distributes the difference of the instance's prediction and the datasets average prediction among the features.

Details

For more details on the algorithm see https://christophm.github.io/interpretable-ml-book/shapley.html

References

Strumbelj, E., Kononenko, I. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems, 41(3), 647-665. https://doi.org/10.1007/s10115-013-0679-x

See also

Shapley

A different way to explain predictions: LocalModel

Super class

iml::InterpretationMethod -> Shapley

Public fields

x.interest

data.frame
Single row with the instance to be explained.

y.hat.interest

numeric
Predicted value for instance of interest.

y.hat.average

numeric(1)
Average predicted value for data X.

sample.size

numeric(1)
The number of times coalitions/marginals are sampled from data X. The higher the more accurate the explanations become.

Methods

Public methods

Inherited methods

Method new()

Create a Shapley object

Usage

Shapley$new(predictor, x.interest = NULL, sample.size = 100)

Arguments

predictor

Predictor
The object (created with Predictor$new()) holding the machine learning model and the data.

x.interest

data.frame
Single row with the instance to be explained.

sample.size

numeric(1)
The number of Monte Carlo samples for estimating the Shapley value.

Returns

data.frame
data.frame with the Shapley values (phi) per feature.


Method explain()

Set a new data point which to explain.

Usage

Shapley$explain(x.interest)

Arguments

x.interest

data.frame
Single row with the instance to be explained.


Method clone()

The objects of this class are cloneable with this method.

Usage

Shapley$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library("rpart") # First we fit a machine learning model on the Boston housing data data("Boston", package = "MASS") rf <- rpart(medv ~ ., data = Boston) X <- Boston[-which(names(Boston) == "medv")] mod <- Predictor$new(rf, data = X) # Then we explain the first instance of the dataset with the Shapley method: x.interest <- X[1, ] shapley <- Shapley$new(mod, x.interest = x.interest) shapley
#> Interpretation method: Shapley #> Predicted value: 27.427273, Average prediction: 22.532806 (diff = 4.894466) #> #> Analysed predictor: #> Prediction task: unknown #> #> #> Analysed data: #> Sampling from data.frame with 506 rows and 13 columns. #> #> #> Head of results: #> feature phi phi.var feature.value #> 1 crim 0.3095547 1.516409 crim=0.00632 #> 2 zn 0.0000000 0.000000 zn=18 #> 3 indus 0.0000000 0.000000 indus=2.31 #> 4 chas 0.0000000 0.000000 chas=0 #> 5 nox 0.0000000 0.000000 nox=0.538 #> 6 rm 2.2868013 23.014323 rm=6.575
# Look at the results in a table shapley$results
#> feature phi phi.var feature.value #> 1 crim 0.3095547 1.516409 crim=0.00632 #> 2 zn 0.0000000 0.000000 zn=18 #> 3 indus 0.0000000 0.000000 indus=2.31 #> 4 chas 0.0000000 0.000000 chas=0 #> 5 nox 0.0000000 0.000000 nox=0.538 #> 6 rm 2.2868013 23.014323 rm=6.575 #> 7 age 0.0000000 0.000000 age=65.2 #> 8 dis -0.5863443 6.867283 dis=4.09 #> 9 rad 0.0000000 0.000000 rad=1 #> 10 tax 0.0000000 0.000000 tax=296 #> 11 ptratio 0.0000000 0.000000 ptratio=15.3 #> 12 black 0.0000000 0.000000 black=396.9 #> 13 lstat 4.1406006 38.654545 lstat=4.98
# Or as a plot plot(shapley)
# Explain another instance shapley$explain(X[2, ]) plot(shapley)
if (FALSE) { # Shapley() also works with multiclass classification rf <- rpart(Species ~ ., data = iris) X <- iris[-which(names(iris) == "Species")] mod <- Predictor$new(rf, data = X, type = "prob") # Then we explain the first instance of the dataset with the Shapley() method: shapley <- Shapley$new(mod, x.interest = X[1, ]) shapley$results plot(shapley) # You can also focus on one class mod <- Predictor$new(rf, data = X, type = "prob", class = "setosa") shapley <- Shapley$new(mod, x.interest = X[1, ]) shapley$results plot(shapley) }