`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.

`R6Class`

object.

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

plot(shapley) shapley$results print(shapley) shapley$explain(x.interest)

For Shapley$new():

- 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.

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

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

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

- 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`

- explain(x.interest)
method to set a new data point which to explain.

- plot()
method to plot the Shapley value. See plot.Shapley

`clone()`

[internal] method to clone the R6 object.

`initialize()`

[internal] method to initialize the R6 object.

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

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

A different way to explain predictions: LocalModel

# NOT RUN { if (require("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 # Look at the results in a table shapley$results # Or as a plot plot(shapley) # Explain another instance shapley$explain(X[2,]) plot(shapley) # } # NOT RUN { # 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) # } # NOT RUN { } # }