ranger (version 0.16.0)

predict.ranger: Ranger prediction


Prediction with new data and a saved forest from Ranger.


# S3 method for ranger
  data = NULL,
  predict.all = FALSE,
  num.trees = object$num.trees,
  type = "response",
  se.method = "infjack",
  quantiles = c(0.1, 0.5, 0.9),
  what = NULL,
  seed = NULL,
  num.threads = NULL,
  verbose = TRUE,


Object of class ranger.prediction with elements

predictionsPredicted classes/values (only for classification and regression)
unique.death.timesUnique death times (only for survival).
chfEstimated cumulative hazard function for each sample (only for survival).
survivalEstimated survival function for each sample (only for survival).
num.treesNumber of trees.
num.independent.variablesNumber of independent variables.
treetypeType of forest/tree. Classification, regression or survival.
num.samplesNumber of samples.



Ranger ranger object.


New test data of class data.frame or gwaa.data (GenABEL).


Return individual predictions for each tree instead of aggregated predictions for all trees. Return a matrix (sample x tree) for classification and regression, a 3d array for probability estimation (sample x class x tree) and survival (sample x time x tree).


Number of trees used for prediction. The first num.trees in the forest are used.


Type of prediction. One of 'response', 'se', 'terminalNodes', 'quantiles' with default 'response'. See below for details.


Method to compute standard errors. One of 'jack', 'infjack' with default 'infjack'. Only applicable if type = 'se'. See below for details.


Vector of quantiles for quantile prediction. Set type = 'quantiles' to use.


User specified function for quantile prediction used instead of quantile. Must return numeric vector, see examples.


Random seed. Default is NULL, which generates the seed from R. Set to 0 to ignore the R seed. The seed is used in case of ties in classification mode.


Number of threads. Default is number of CPUs available.


Verbose output on or off.


further arguments passed to or from other methods.


Marvin N. Wright


For type = 'response' (the default), the predicted classes (classification), predicted numeric values (regression), predicted probabilities (probability estimation) or survival probabilities (survival) are returned. For type = 'se', the standard error of the predictions are returned (regression only). The jackknife-after-bootstrap or infinitesimal jackknife for bagging is used to estimate the standard errors based on out-of-bag predictions. See Wager et al. (2014) for details. For type = 'terminalNodes', the IDs of the terminal node in each tree for each observation in the given dataset are returned. For type = 'quantiles', the selected quantiles for each observation are estimated. See Meinshausen (2006) for details.

If type = 'se' is selected, the method to estimate the variances can be chosen with se.method. Set se.method = 'jack' for jackknife-after-bootstrap and se.method = 'infjack' for the infinitesimal jackknife for bagging.

For classification and predict.all = TRUE, a factor levels are returned as numerics. To retrieve the corresponding factor levels, use rf$forest$levels, if rf is the ranger object.


  • Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. tools:::Rd_expr_doi("10.18637/jss.v077.i01").

  • Wager, S., Hastie T., & Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. J Mach Learn Res 15:1625-1651. https://jmlr.org/papers/v15/wager14a.html.

  • Meinshausen (2006). Quantile Regression Forests. J Mach Learn Res 7:983-999. https://www.jmlr.org/papers/v7/meinshausen06a.html.

See Also



Run this code
## Classification forest
ranger(Species ~ ., data = iris)
train.idx <- sample(nrow(iris), 2/3 * nrow(iris))
iris.train <- iris[train.idx, ]
iris.test <- iris[-train.idx, ]
rg.iris <- ranger(Species ~ ., data = iris.train)
pred.iris <- predict(rg.iris, data = iris.test)
table(iris.test$Species, pred.iris$predictions)

## Quantile regression forest
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles", quantiles = c(0.1, 0.5, 0.9))

## Quantile regression forest with user-specified function
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles", 
                what = function(x) sample(x, 10, replace = TRUE))

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