scoring {targeted}

Predictive model scoring

Description

Predictive model scoring

Usage

scoring(
  response,
  ...,
  type = "quantitative",
  levels = NULL,
  metrics = NULL,
  weights = NULL,
  names = NULL,
  messages = 1
)

Arguments

response

Observed response

...

model predictions (continuous predictions or class probabilities (matrices))

type

continuous or categorical response (the latter is automatically chosen if response is a factor, otherwise a continuous response is assumed)

levels

(optional) unique levels in response variable

metrics

which metrics to report

weights

optional frequency weights

names

optional names of models coments (given as ..., alternatively these can be named arguments)

messages

controls amount of messages/warnings (0: none)

Value

Numeric matrix of dimension m x p, where m is the number of different models and p is the number of model metrics

Examples

data(iris)
set.seed(1)
dat <- csplit(iris,2)
g1 <- NB(Species ~ Sepal.Width + Petal.Length, data=dat[[1]])
g2 <- NB(Species ~ Sepal.Width, data=dat[[1]])
pr1 <- predict(g1, newdata=dat[[2]], wide=TRUE)
pr2 <- predict(g2, newdata=dat[[2]], wide=TRUE)
table(colnames(pr1)[apply(pr1,1,which.max)], dat[[2]]$Species)
table(colnames(pr2)[apply(pr2,1,which.max)], dat[[2]]$Species)
scoring(dat[[2]]$Species, pr1=pr1, pr2=pr2)
## quantitative response:
scoring(response=1:10, prediction=rnorm(1:10))