Individual Conditional Expectations

Disaggregated partial dependencies, see reference. The plot method supports up to two grouping variables via BY.

ice(object, ...)

# S3 method for default
ice(
  object,
  v,
  X,
  pred_fun = stats::predict,
  BY = NULL,
  grid = NULL,
  grid_size = 49L,
  trim = c(0.01, 0.99),
  strategy = c("uniform", "quantile"),
  na.rm = TRUE,
  n_max = 100L,
  ...
)

# S3 method for ranger
ice(
  object,
  v,
  X,
  pred_fun = function(m, X, ...) stats::predict(m, X, ...)$predictions,
  BY = NULL,
  grid = NULL,
  grid_size = 49L,
  trim = c(0.01, 0.99),
  strategy = c("uniform", "quantile"),
  na.rm = TRUE,
  n_max = 100L,
  ...
)

# S3 method for explainer
ice(
  object,
  v = v,
  X = object[["data"]],
  pred_fun = object[["predict_function"]],
  BY = NULL,
  grid = NULL,
  grid_size = 49L,
  trim = c(0.01, 0.99),
  strategy = c("uniform", "quantile"),
  na.rm = TRUE,
  n_max = 100L,
  ...
)

Arguments

object: Fitted model object.
...: Additional arguments passed to pred_fun(object, X, ...), for instance type = "response" in a glm() model, or reshape = TRUE in a multiclass XGBoost model.
v: One or more column names over which you want to calculate the ICE.
X: A data.frame or matrix serving as background dataset.
pred_fun: Prediction function of the form function(object, X, ...), providing \(K \ge 1\) predictions per row. Its first argument represents the model object, its second argument a data structure like X. Additional arguments (such as type = "response" in a GLM, or reshape = TRUE in a multiclass XGBoost model) can be passed via .... The default, stats::predict(), will work in most cases.
BY: Optional grouping vector/matrix/data.frame (up to two columns), or up to two column names. Unlike with partial_dep(), these variables are not binned. The first variable is visualized on the color scale, while the second one goes into a facet_wrap(). Thus, make sure that the second variable is discrete.
grid: Evaluation grid. A vector (if length(v) == 1L), or a matrix/data.frame otherwise. If NULL, calculated via multivariate_grid().
grid_size: Controls the approximate grid size. If x has p columns, then each (non-discrete) column will be reduced to about the p-th root of grid_size values.
trim: The default c(0.01, 0.99) means that values outside the 1% and 99% quantiles of non-discrete numeric columns are removed before calculation of grid values. Set to 0:1 for no trimming.
strategy: How to find grid values of non-discrete numeric columns? Either "uniform" or "quantile", see description of univariate_grid().
na.rm: Should missing values be dropped from the grid? Default is TRUE.
n_max: If X has more than n_max rows, a random sample of n_max rows is selected from X. In this case, set a random seed for reproducibility.

Value

An object of class "ice" containing these elements:

data: data.frame containing the ice values.
grid: Vector, matrix or data.frame of grid values.
v: Same as input v.
K: Number of columns of prediction matrix.
pred_names: Column names of prediction matrix.
by_names: Column name(s) of grouping variable(s) (or NULL).

Methods (by class)

ice(default): Default method.
ice(ranger): Method for "ranger" models.
ice(explainer): Method for DALEX "explainer".

References

Goldstein, Alex, and Adam Kapelner and Justin Bleich and Emil Pitkin. Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation. Journal of Computational and Graphical Statistics, 24, no. 1 (2015): 44-65.

Examples

# MODEL 1: Linear regression
fit <- lm(Sepal.Length ~ . + Species * Petal.Length, data = iris)
plot(ice(fit, v = "Sepal.Width", X = iris))


# Stratified by one variable
ic <- ice(fit, v = "Petal.Length", X = iris, BY = "Species")
ic
#> 'ice' object (4300 rows). Extract via $data. Top rows:
#> 
#>   obs_ Petal.Length        y    Species
#> 1    1            1 4.667167     setosa
#> 2    2            1 4.840846     setosa
#> 3    3            1 3.452847 versicolor
plot(ic)

plot(ic, center = TRUE)


if (FALSE) {
# Stratified by two variables (the second one goes into facets)
ic <- ice(fit, v = "Petal.Length", X = iris, BY = c("Petal.Width", "Species"))
plot(ic)
plot(ic, center = TRUE)

# MODEL 2: Multi-response linear regression
fit <- lm(as.matrix(iris[, 1:2]) ~ Petal.Length + Petal.Width * Species, data = iris)
ic <- ice(fit, v = "Petal.Width", X = iris, BY = iris$Species)
plot(ic)
plot(ic, center = TRUE)
plot(ic, swap_dim = TRUE)
}

# MODEL 3: Gamma GLM -> pass options to predict() via ...
fit <- glm(Sepal.Length ~ ., data = iris, family = Gamma(link = log))
plot(ice(fit, v = "Petal.Length", X = iris, BY = "Species"))

plot(ice(fit, v = "Petal.Length", X = iris, type = "response", BY = "Species"))