nullModel: Fit a simple, non-informative model

In caret: Classification and Regression Training

Description

Fit a single mean or largest class model

Usage

nullModel(x, ...)

## Default S3 method:
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)

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.

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.

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))

caret documentation built on May 2, 2019, 5:47 p.m.