caret (version 4.69)

nullModel: Fit a simple, non-informative model

Description

Fit a single mean or largest class model

Usage

nullModel(x, ...)
## S3 method for class 'default':
nullModel(x = NULL, y, ...)
## S3 method for class 'nullModel':
predict(object, newdata = NULL, type = NULL, ...)

Arguments

x

An optional matrix or data frame of predictors. These values are not used in the model fit

y

A numeric vector (for regression) or factor (for classification) of outcomes

...

Optional arguments (not yet used)

object

An object of class nullModel

newdata

A matrix or data frame of predictors (only used to determine the number of predictions to return)

type

Either "raw" (for regression), "class" or "prob" (for classification)

Value

• The output of nullModel is a list of class nullModel with elements
• call
  the function call
• value
  the mean of y or the most prevalent class
• levels
  when y is a factor, a vector of levels. NULL otherwise
• pct
  when y is a factor, a data frame with a column for each class (NULL otherwise). The column for the most prevalent class has the proportion of the training samples with that class (the other columns are zero).
• n
  the number of elements in y
• predict.nullModel returns a either a factor or numeric vector depending on the class of y. All predictions are always the same.

Details

nullModel emulates other model building functions, but returns the simplest model possible given a training set: a single mean for numeric outcomes and the most prevalent class for factor outcomes. When class probabilities are requested, the percentage of the training set samples with the most prevalent class is returned.

Examples

outcome <- factor(
                  sample(letters[1:2], 
                         size = 100, 
                         prob = c(.1, .9), 
                         replace = TRUE))
useless <- nullModel(y = outcome)
useless
predict(useless, matrix(NA, nrow = 10))