FeatureEffects: Effect of a feature on predictions

In christophM/iml: Interpretable Machine Learning

Effect of a feature on predictions

Description

FeatureEffects computes and plots feature effects of multiple features at once.

Details

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

Parallelization is supported via package 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 future package for more parallel backend options.

Super class

iml::InterpretationMethod -> FeatureEffects

Public fields

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.

Methods

Public methods

• FeatureEffects$new()

• FeatureEffects$clone()

Inherited methods

• iml::InterpretationMethod$plot()
• iml::InterpretationMethod$print()

---

Method new()

Create a FeatureEffects object

Usage

FeatureEffects$new(
  predictor,
  features = NULL,
  method = "ale",
  center.at = NULL,
  grid.size = 20
)

Arguments

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.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FeatureEffects$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

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

See Also

plot.FeatureEffects

Examples

# 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)

christophM/iml documentation built on April 23, 2024, 1:25 a.m.