Split Conformal Prediction Sets for Classification

[Deprecated] conformal_class_split() is identical to conformal_lac() and is deprecated. Use conformal_lac() instead.

Usage

conformal_class_split(
  x,
  y,
  model,
  x_new,
  alpha = 0.1,
  cal_fraction = 0.5,
  seed = NULL
)

Arguments

x

A numeric matrix or data frame of predictor variables.

y

A factor (or character/integer vector coerced to factor) of class labels.

model

A make_model() specification with type = "classification", or a fitted model object that produces class probabilities.

x_new

A numeric matrix or data frame of new predictor variables.

alpha

Miscoverage level. Default 0.10 gives 90 percent prediction sets.

cal_fraction

Fraction of data used for calibration. Default 0.5.

seed

Optional random seed.

Value

A predictset_class object. See conformal_lac() for details.

References

Sadinle, M., Lei, J. and Wasserman, L. (2019). Least ambiguous set-valued classifiers with bounded error levels. Journal of the American Statistical Association, 114(525), 223-234. doi:10.1080/01621459.2017.1395341

Examples

set.seed(42)
n <- 300
x <- matrix(rnorm(n * 4), ncol = 4)
y <- factor(ifelse(x[,1] + x[,2] > 0, "A", "B"))
x_new <- matrix(rnorm(50 * 4), ncol = 4)

clf <- make_model(
  train_fun = function(x, y) {
    df <- data.frame(y = y, x)
    glm(y ~ ., data = df, family = "binomial")
  },
  predict_fun = function(object, x_new) {
    df <- as.data.frame(x_new)
    names(df) <- paste0("X", seq_len(ncol(x_new)))
    p <- predict(object, newdata = df, type = "response")
    cbind(A = 1 - p, B = p)
  },
  type = "classification"
)

# \donttest{
suppressWarnings(
  result <- conformal_class_split(x, y, model = clf, x_new = x_new)
)
# }