ranger (version 0.16.0)

csrf: Case-specific random forests.

Description

In case-specific random forests (CSRF), random forests are built specific to the cases of interest. Instead of using equal probabilities, the cases are weighted according to their difference to the case of interest.

Usage

csrf(
  formula,
  training_data,
  test_data,
  params1 = list(),
  params2 = list(),
  verbose = FALSE
)

Value

Predictions for the test dataset.

Arguments

formula

Object of class formula or character describing the model to fit.

training_data

Training data of class data.frame.

test_data

Test data of class data.frame.

params1

Parameters for the proximity random forest grown in the first step.

params2

Parameters for the prediction random forests grown in the second step.

verbose

Logical indicating whether or not to print computation progress.

Author

Marvin N. Wright

Details

The algorithm consists of 3 steps:

1. Grow a random forest on the training data

2. For each observation of interest (test data), the weights of all training observations are computed by counting the number of trees in which both observations are in the same terminal node.

3. For each test observation, grow a weighted random forest on the training data, using the weights obtained in step 2. Predict the outcome of the test observation as usual.

In total, n+1 random forests are grown, where n is the number observations in the test dataset. For details, see Xu et al. (2014).

References

Xu, R., Nettleton, D. & Nordman, D.J. (2014). Case-specific random forests. J Comp Graph Stat 25:49-65. tools:::Rd_expr_doi("10.1080/10618600.2014.983641").

Examples

## Split in training and test data
train.idx <- sample(nrow(iris), 2/3 * nrow(iris))
iris.train <- iris[train.idx, ]
iris.test <- iris[-train.idx, ]

## Run case-specific RF
csrf(Species ~ ., training_data = iris.train, test_data = iris.test, 
     params1 = list(num.trees = 50, mtry = 4), 
     params2 = list(num.trees = 5))