costsensitive (version 0.1.2.10)

weighted.one.vs.rest: Weighted One-Vs-Rest

Description

Creates a cost-sensitive classifier by creating one classifier per class to predict cost. Takes as input a classifier accepting observation weights. The objective is to create a model that would predict the class with the minimum cost.

Usage

weighted.one.vs.rest(X, C, classifier, predict_type_prob = "prob",
  wap_weights = FALSE, nthreads = 1, ...)

Arguments

X

The data (covariates/features).

C

matrix(n_samples, n_classes) Costs for each class for each observation.

classifier

function(X, y, weights=w, ...) -> object, that would create a classifier with method `predict`. The `y` vector passed to it is of class `integer` with values 0/1 only.

predict_type_prob

argument to pass to method `predict` from the classifier passed to `classifier` in order to output probabilities or numeric scores instead of classes (i.e. `predict(object, newdata, type=predict_type_prob`)).

wap_weights

Whether to use the weighting technique from the `Weighted-All-Pairs` algorithm.

nthreads

Number of parallel threads to use (not available on Windows systems). Note that, unlike the Python version, this is not a shared memory model and each additional thread will require more memory from the system. Not recommended to use when the algorithm is itself parallelized.

...

Extra arguments to pass to `classifier`.

References

Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B. (2005, August). Error limiting reductions between classification tasks.

Examples

# NOT RUN {
library(costsensitive)
wrapped.logistic <- function(X, y, weights, ...) {
	return(glm(y ~ ., data = X, weights = weights, family = "quasibinomial", ...))
}
set.seed(1)
X <- data.frame(feature1 = rnorm(100), feature2 = rnorm(100), feature3 = runif(100))
C <- data.frame(cost1 = rgamma(100, 1), cost2 = rgamma(100, 1), cost3 = rgamma(100, 1))
model <- weighted.one.vs.rest(X, C, wrapped.logistic, predict_type_prob = "response")
predict(model, X, type = "class")
predict(model, X, type = "score")
print(model)

# }