Predict method for eXtreme Gradient Boosting model

Predicted values based on either xgboost model or model handle object.

# S3 method for xgb.Booster
predict(object, newdata, missing = NA,
  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE,
  reshape = FALSE, ...)

# S3 method for xgb.Booster.handle predict(object, ...)


Object of class xgb.Booster or xgb.Booster.handle


takes matrix, dgCMatrix, local data file or xgb.DMatrix.


Missing is only used when input is dense matrix. Pick a float value that represents missing values in data (e.g., sometimes 0 or some other extreme value is used).


whether the prediction should be returned in the for of original untransformed sum of predictions from boosting iterations' results. E.g., setting outputmargin=TRUE for logistic regression would result in predictions for log-odds instead of probabilities.


limit the number of model's trees or boosting iterations used in prediction (see Details). It will use all the trees by default (NULL value).


whether predict leaf index instead.


whether to reshape the vector of predictions to a matrix form when there are several prediction outputs per case. This option has no effect when predleaf = TRUE.


Parameters passed to predict.xgb.Booster


Note that ntreelimit is not necessarily equal to the number of boosting iterations and it is not necessarily equal to the number of trees in a model. E.g., in a random forest-like model, ntreelimit would limit the number of trees. But for multiclass classification, there are multiple trees per iteration, but ntreelimit limits the number of boosting iterations.

Also note that ntreelimit would currently do nothing for predictions from gblinear, since gblinear doesn't keep its boosting history.

One possible practical applications of the predleaf option is to use the model as a generator of new features which capture non-linearity and interactions, e.g., as implemented in xgb.create.features.


For regression or binary classification, it returns a vector of length nrows(newdata). For multiclass classification, either a num_class * nrows(newdata) vector or a (nrows(newdata), num_class) dimension matrix is returned, depending on the reshape value.

When predleaf = TRUE, the output is a matrix object with the number of columns corresponding to the number of trees.

See Also


  • predict.xgb.Booster
  • predict.xgb.Booster.handle
## binary classification:

data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
train <- agaricus.train
test <- agaricus.test

bst <- xgboost(data = train$data, label = train$label, max_depth = 2, 
               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
# use all trees by default
pred <- predict(bst, test$data)
# use only the 1st tree
pred <- predict(bst, test$data, ntreelimit = 1)

## multiclass classification in iris dataset:

lb <- as.numeric(iris$Species) - 1
num_class <- 3
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
               objective = "multi:softprob", num_class = num_class)
# predict for softmax returns num_class probability numbers per case:
pred <- predict(bst, as.matrix(iris[, -5]))
# reshape it to a num_class-columns matrix
pred <- matrix(pred, ncol=num_class, byrow=TRUE)
# convert the probabilities to softmax labels
pred_labels <- max.col(pred) - 1
# the following should result in the same error as seen in the last iteration
sum(pred_labels != lb)/length(lb)

# compare that to the predictions from softmax:
bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
               max_depth = 4, eta = 0.5, nthread = 2, nrounds = 10, subsample = 0.5,
               objective = "multi:softmax", num_class = num_class)
pred <- predict(bst, as.matrix(iris[, -5]))
all.equal(pred, pred_labels)
# prediction from using only 5 iterations should result 
# in the same error as seen in iteration 5:
pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5)
sum(pred5 != lb)/length(lb)

## random forest-like model of 25 trees for binary classification:

bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
               nthread = 2, nrounds = 1, objective = "binary:logistic",
               num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
# Inspect the prediction error vs number of trees:
lb <- test$label
dtest <- xgb.DMatrix(test$data, label=lb)
err <- sapply(1:25, function(n) {
  pred <- predict(bst, dtest, ntreelimit=n)
  sum((pred > 0.5) != lb)/length(lb)
plot(err, type='l', ylim=c(0,0.1), xlab='#trees')

# }
Documentation reproduced from package xgboost, version, License: Apache License (== 2.0) | file LICENSE

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