regression.one.vs.rest: Regression One-Vs-Rest

In costsensitive: Cost-Sensitive Multi-Class Classification

Description

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

Usage

regression.one.vs.rest(X, C, regressor, nthreads = 1, ...)

Arguments

X	The data (covariates/features).
C	matrix(n_samples, n_classes) Costs for each class for each observation.
regressor	function(X, y, ...) -> object, that would create regressor with method 'predict'.
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 'regressor'.

References

Beygelzimer, A., Langford, J., & Zadrozny, B. (2008). Machine learning techniques-reductions between prediction quality metrics.

Examples

library(costsensitive)
wrapped.lm <- function(X, y, ...) {
	return(lm(y ~ ., data = X, ...))
}
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 <- regression.one.vs.rest(X, C, wrapped.lm)
predict(model, X, type = "class")
predict(model, X, type = "score")
print(model)

costsensitive documentation built on July 28, 2019, 5:02 p.m.