y <- 1:10
pred <- c(1, 1:9)
w <- 1:10
r_squared(y, pred)
r_squared(y, pred, w = w)
r_squared(y, pred, w = w, deviance_function = deviance_gamma)
r_squared_gamma(y, pred, w = w)
# Poisson situation
y2 <- 0:2
pred2 <- c(0.1, 1, 2)
r_squared(y2, pred2, deviance_function = deviance_poisson)
r_squared_poisson(y2, pred2)
# Binary (probabilistic) classification
y3 <- c(0, 0, 1, 1)
pred3 <- c(0.1, 0.1, 0.9, 0.8)
r_squared_bernoulli(y3, pred3, w = 1:4)
# With respect to 'own' deviance formula
myTweedie <- function(actual, predicted, w = NULL, ...) {
deviance_tweedie(actual, predicted, w, tweedie_p = 1.5, ...)
}
r_squared(y, pred, deviance_function = myTweedie)
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