iml (version 0.9.0)

LocalModel: LocalModel

Description

LocalModel fits locally weighted linear regression models (logistic regression for classification) to explain single predictions of a prediction model.

Format

R6Class object.

Usage

lime = LocalModel$new(predictor, x.interest = NULL, dist.fun = "gower",  
                      kernel.width = NULL, k = 3)

plot(lime) predict(lime, newdata) lime$results lime$explain(x.interest) print(lime)

Arguments

For LocalModel$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.

dist.fun:

(`character(1)`) The name of the distance function for computing proximities (weights in the linear model). Defaults to "gower". Otherwise will be forwarded to [stats::dist].

kernel.width:

(`numeric(1)`) The width of the kernel for the proximity computation. Only used if dist.fun is not 'gower'.

k:

(`numeric(1)`) The (maximum) number of features to be used for the surrogate model.

Fields

best.fit.index:

(`numeric(1)`) The index of the best glmnet fit.

k:

(`numeric(1)`) The number of features as set by the user.

model:

(glmnet) The fitted local model.

predictor:

(Predictor) The prediction model that was analysed.

results:

(data.frame) Results with the feature names (feature) and contributions to the prediction

x.interest:

(data.frame) The instance to be explained. See Examples for usage.

Methods

explain(x.interest)

method to set a new data point which to explain.

plot()

method to plot the LocalModel feature effects. See plot.LocalModel

predict()

method to predict new data with the local model See also predict.LocalModel

clone()

[internal] method to clone the R6 object.

initialize()

[internal] method to initialize the R6 object.

Details

A weighted glm is fitted with the machine learning model prediction as target. Data points are weighted by their proximity to the instance to be explained, using the gower proximity measure. L1-regularisation is used to make the results sparse. The resulting model can be seen as a surrogate for the machine learning model, which is only valid for that one point. Categorical features are binarized, depending on the category of the instance to be explained: 1 if the category is the same, 0 otherwise. To learn more about local models, read the Interpretable Machine Learning book: https://christophm.github.io/interpretable-ml-book/lime.html

The approach is similar to LIME, but has the following differences:

• Distance measure: Uses as default the gower proximity (= 1 - gower distance) instead of a kernel based on the Euclidean distance. Has the advantage to have a meaningful neighbourhood and no kernel width to tune. When the distance is not "gower", then the stats::dist() function with the chosen method will be used, and turned into a similarity measure: sqrt(exp(-(distance^2) / (kernel.width^2))).

• Sampling: Uses the original data instead of sampling from normal distributions. Has the advantage to follow the original data distribution.

• Visualisation: Plots effects instead of betas. Both are the same for binary features, but ared different for numerical features. For numerical features, plotting the betas makes no sense, because a negative beta might still increase the prediction when the feature value is also negative.

To learn more about local surrogate models, read the Interpretable Machine Learning book: https://christophm.github.io/interpretable-ml-book/lime.html

References

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. Retrieved from http://arxiv.org/abs/1602.04938

Gower, J. C. (1971), "A general coefficient of similarity and some of its properties". Biometrics, 27, 623--637.

See Also

plot.LocalModel and predict.LocalModel

Shapley can also be used to explain single predictions

The package `lime` with the original implementation

Examples

# NOT RUN {
if (require("randomForest")) {
# First we fit a machine learning model on the Boston housing data
data("Boston", package  = "MASS")
X = Boston[-which(names(Boston) == "medv")]
rf = randomForest(medv ~ ., data = Boston, ntree = 50)
mod = Predictor$new(rf, data = X)

# Explain the first instance of the dataset with the LocalModel method:
x.interest = X[1,]
lemon = LocalModel$new(mod, x.interest = x.interest, k = 2)
lemon

# Look at the results in a table
lemon$results
# Or as a plot
plot(lemon)

# Reuse the object with a new instance to explain
lemon$x.interest
lemon$explain(X[2,])
lemon$x.interest
plot(lemon)
  
# LocalModel also works with multiclass classification
rf = randomForest(Species ~ ., data= iris, ntree=50)
X = iris[-which(names(iris) == 'Species')]
mod = Predictor$new(rf, data = X, type = "prob", class = "setosa")

# Then we explain the first instance of the dataset with the LocalModel method:
lemon = LocalModel$new(mod, x.interest = X[1,], k = 2)
lemon$results
plot(lemon) 
}
# }