targeted (version 0.6)

scoring: Predictive model scoring

Description

Predictive model scoring

Usage

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

Value

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

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) character vector of the model names in the output. If omitted these will be taken from the names of the ellipsis argument (...)

object

optional model object

newdata

(optional) data.frame on which to evaluate the model performance

messages

controls amount of messages/warnings (0: none)

Examples

data(iris)
set.seed(1)
dat <- lava::csplit(iris,2)
g1 <- naivebayes(Species ~ Sepal.Width + Petal.Length, data=dat[[1]])
g2 <- naivebayes(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))