Ranger prediction

Prediction with new data and a saved forest from Ranger.

Usage

# S3 method for class 'ranger'
predict(
  object,
  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,
  ...
)

Arguments

object: Ranger ranger object.
data: New test data of class data.frame or gwaa.data (GenABEL).
predict.all: 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).
num.trees: Number of trees used for prediction. The first num.trees in the forest are used.
type: Type of prediction. One of 'response', 'se', 'terminalNodes', 'quantiles' with default 'response'. See below for details.
se.method: Method to compute standard errors. One of 'jack', 'infjack' with default 'infjack'. Only applicable if type = 'se'. See below for details.
quantiles: Vector of quantiles for quantile prediction. Set type = 'quantiles' to use.
what: User specified function for quantile prediction used instead of quantile. Must return numeric vector, see examples.
seed: 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.
num.threads: Number of threads. Use 0 for all available cores. Default is 2 if not set by options/environment variables (see below).
verbose: Verbose output on or off.
...: further arguments passed to or from other methods.

Value

Object of class ranger.prediction with elements

`predictions`	Predicted classes/values (only for classification and regression)
`unique.death.times`	Unique death times (only for survival).
`chf`	Estimated cumulative hazard function for each sample (only for survival).
`survival`	Estimated survival function for each sample (only for survival).
`num.trees`	Number of trees.
`num.independent.variables`	Number of independent variables.
`treetype`	Type of forest/tree. Classification, regression or survival.
`num.samples`	Number of samples.

Details

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.

By default, ranger uses 2 threads. The default can be changed with: (1) num.threads in ranger/predict call, (2) environment variable R_RANGER_NUM_THREADS, (3) options(ranger.num.threads = N), (4) options(Ncpus = N), with precedence in that order.

References

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. 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.

Author

Marvin N. Wright

Examples

## Classification forest
ranger(Species ~ ., data = iris)
#> Ranger result
#> 
#> Call:
#>  ranger(Species ~ ., data = iris) 
#> 
#> Type:                             Classification 
#> Number of trees:                  500 
#> Sample size:                      150 
#> Number of independent variables:  4 
#> Mtry:                             2 
#> Target node size:                 1 
#> Variable importance mode:         none 
#> Splitrule:                        gini 
#> OOB prediction error:             4.67 % 
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)
#>             
#>              setosa versicolor virginica
#>   setosa         14          0         0
#>   versicolor      0         13         1
#>   virginica       0          3        19

## 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))
pred$predictions
#>      quantile= 0.1 quantile= 0.5 quantile= 0.9
#> [1,]          21.0          24.4          32.4
#> [2,]          21.0          22.8          32.4
#> [3,]          13.3          17.3          30.4
#> [4,]          15.2          21.0          22.8
#> [5,]          13.3          14.3          19.2
#> [6,]          21.0          22.8          32.4

## 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))
pred$predictions
#>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 30.4 30.4 30.4 21.0 27.3 32.4 27.3 30.4 24.4  21.0
#> [2,] 22.8 22.8 33.9 19.2 30.4 21.5 22.8 30.4 21.0  30.4
#> [3,] 14.3 13.3 14.3 33.9 14.7 19.2 17.8 10.4 14.3  27.3
#> [4,] 21.0 21.5 21.0 21.5 17.8 21.0 22.8 22.8 21.0  30.4
#> [5,] 14.7 13.3 13.3 14.3 10.4 15.2 14.7 18.7 19.2  14.3
#> [6,] 24.4 22.8 27.3 27.3 21.0 27.3 22.8 22.8 22.8  32.4