Ranger prediction
Prediction with new data and a saved forest from Ranger.
## 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, ... )
object |
Ranger |
data |
New test data of class |
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 |
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 |
what |
User specified function for quantile prediction used instead of |
seed |
Random seed. Default is |
num.threads |
Number of threads. Default is number of CPUs available. |
verbose |
Verbose output on or off. |
... |
further arguments passed to or from other methods. |
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$levels, if rf is the ranger object.
Object of class ranger.prediction with elements
predictions |
Predicted classes/values (only for classification and regression) |
unique.death.times |
Unique death times (only for survival). |
chf |
Estimated cumulative hazard function for each sample (only for survival). |
survival |
Estimated survival function for each sample (only for survival). |
num.trees |
Number of trees. |
num.independent.variables |
Number of independent variables. |
treetype |
Type of forest/tree. Classification, regression or survival. |
num.samples |
Number of samples. |
Marvin N. Wright
Wright, M. N. & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. J Stat Softw 77:1-17. https://doi.org/10.18637/jss.v077.i01.
Wager, S., Hastie T., & Efron, B. (2014). Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife. J Mach Learn Res 15:1625-1651. http://jmlr.org/papers/v15/wager14a.html.
Meinshausen (2006). Quantile Regression Forests. J Mach Learn Res 7:983-999. http://www.jmlr.org/papers/v7/meinshausen06a.html.
## Classification forest
ranger(Species ~ ., data = iris)
train.idx <- sample(nrow(iris), 2/3 * nrow(iris))
iris.train <- iris[train.idx, ]
iris.test <- iris[-train.idx, ]
rg.iris <- ranger(Species ~ ., data = iris.train)
pred.iris <- predict(rg.iris, data = iris.test)
table(iris.test$Species, pred.iris$predictions)
## Quantile regression forest
rf <- ranger(mpg ~ ., mtcars[1:26, ], quantreg = TRUE)
pred <- predict(rf, mtcars[27:32, ], type = "quantiles", quantiles = c(0.1, 0.5, 0.9))
pred$predictions
## 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$predictionsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.