Effect of a feature on predictions
FeatureEffects computes and plots feature effects of multiple features at once.
FeatureEffects computes the effects for all given features on the model prediction. FeatureEffects is a convenience class that calls FeatureEffect multiple times. See ?FeatureEffect for details what's actually computed.
Only first-order effects can be computed with the FeatureEffects interface. If you are interested in the visualization of interactions between two features, directly use FeatureEffect .
Parallelization is supported via package list("future"). To initialize future-based parallelization, select an appropriate backend and specify the amount of workers. For example, to use a PSOCK based cluster backend do:
future::plan(multisession, workers = 2)
<iml function here>
Consult the resources of the list("future") package for more parallel backend options.
# We train a random forest on the Boston dataset: library("rpart") data("Boston", package = "MASS") rf <- rpart(medv ~ ., data = Boston) mod <- Predictor$new(rf, data = Boston) # Compute the accumulated local effects for all features eff <- FeatureEffects$new(mod) eff$plot() ## Not run: # Again, but this time with a partial dependence plot eff <- FeatureEffects$new(mod, method = "pdp") eff$plot() # Only a subset of features eff <- FeatureEffects$new(mod, features = c("nox", "crim")) eff$plot() # You can access each FeatureEffect individually eff.nox <- eff$effects[["nox"]] eff.nox$plot() # FeatureEffects also works with multiclass classification rf <- rpart(Species ~ ., data = iris) mod <- Predictor$new(rf, data = iris, type = "prob") FeatureEffects$new(mod)$plot(ncol = 2) ## End(Not run)
Apley, D. W. 2016. "Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models." ArXiv Preprint.
Friedman, J.H. 2001. "Greedy Function Approximation: A Gradient Boosting Machine." Annals of Statistics 29: 1189-1232.
Goldstein, A., Kapelner, A., Bleich, J., and Pitkin, E. (2013). Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation, 1-22. https://doi.org/10.1080/10618600.2014.907095
plot.FeatureEffects
iml::InterpretationMethod -> FeatureEffects
grid.size: (numeric(1) | numeric(2))
The size of the grid.
method: (character(1))
* "ale" for accumulated local effects,
* "pdp" for partial dependence plot,
* "ice" for individual conditional expectation curves,
* "pdp+ ice" for partial dependence plot and ice curves within the same plot.
effects: (list )
Named list of FeatureEffects.
features: (character())
The names of the features for which the effects were computed.
center.at: numeric
Value at which the plot was centered. Ignored in the case of two features.
new()Create a FeatureEffects object
FeatureEffects$new(
predictor,
features = NULL,
method = "ale",
center.at = NULL,
grid.size = 20
)
predictor: Predictor
The object (created with `Predictor$new()`) holding the machine learning model and the data.
features: (character())
The names of the features for which to compute the feature effects.
method: (character(1))
* 'ale' for accumulated local effects,
* 'pdp' for partial dependence plot,
* 'ice' for individual conditional expectation curves,
* 'pdp+ice' for partial dependence plot and ice curves within the same plot.
center.at: (numeric(1))
Value at which the plot should be centered. Ignored in the case of two features.
grid.size: (numeric(1) | numeric(2))
The size of the grid for evaluating the predictions.
feature: (character(1) | character(2) | numeric(1) | numeric(2))
The feature name or index for which to compute the effects.
clone()The objects of this class are cloneable with this method.
FeatureEffects$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links