train.xgboost {traineR}

train.xgboost

Description

Provides a wrapping function for the xgb.train.

Usage

train.xgboost(
  formula,
  data,
  nrounds,
  watchlist = list(),
  obj = NULL,
  feval = NULL,
  verbose = 1,
  print_every_n = 1L,
  early_stopping_rounds = NULL,
  maximize = NULL,
  save_period = NULL,
  save_name = "xgboost.model",
  xgb_model = NULL,
  callbacks = list(),
  eval_metric = "mlogloss",
  extra_params = NULL,
  booster = "gbtree",
  objective = NULL,
  eta = 0.3,
  gamma = 0,
  max_depth = 6,
  min_child_weight = 1,
  subsample = 1,
  colsample_bytree = 1,
  ...
)

Arguments

formula

a symbolic description of the model to be fit.

data

training dataset. xgb.train accepts only an xgb.DMatrix as the input. xgboost, in addition, also accepts matrix, dgCMatrix, or name of a local data file.

nrounds

max number of boosting iterations.

watchlist

named list of xgb.DMatrix datasets to use for evaluating model performance. Metrics specified in either eval_metric or feval will be computed for each of these datasets during each boosting iteration, and stored in the end as a field named evaluation_log in the resulting object. When either verbose>=1 or cb.print.evaluation callback is engaged, the performance results are continuously printed out during the training. E.g., specifying watchlist=list(validation1=mat1, validation2=mat2) allows to track the performance of each round's model on mat1 and mat2.

obj

customized objective function. Returns gradient and second order gradient with given prediction and dtrain.

feval

custimized evaluation function. Returns list(metric='metric-name', value='metric-value') with given prediction and dtrain.

verbose

If 0, xgboost will stay silent. If 1, it will print information about performance. If 2, some additional information will be printed out. Note that setting verbose > 0 automatically engages the cb.print.evaluation(period=1) callback function.

print_every_n

Print each n-th iteration evaluation messages when verbose>0. Default is 1 which means all messages are printed. This parameter is passed to the cb.print.evaluation callback.

early_stopping_rounds

If NULL, the early stopping function is not triggered. If set to an integer k, training with a validation set will stop if the performance doesn't improve for k rounds. Setting this parameter engages the cb.early.stop callback.

maximize

If feval and early_stopping_rounds are set, then this parameter must be set as well. When it is TRUE, it means the larger the evaluation score the better. This parameter is passed to the cb.early.stop callback.

save_period

when it is non-NULL, model is saved to disk after every save_period rounds, 0 means save at the end. The saving is handled by the cb.save.model callback.

save_name

the name or path for periodically saved model file.

xgb_model

a previously built model to continue the training from. Could be either an object of class xgb.Booster, or its raw data, or the name of a file with a previously saved model.

callbacks

a list of callback functions to perform various task during boosting. See callbacks. Some of the callbacks are automatically created depending on the parameters' values. User can provide either existing or their own callback methods in order to customize the training process.

eval_metric

eval_metric evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.

extra_params

the list of parameters. The complete list of parameters is available at http://xgboost.readthedocs.io/en/latest/parameter.html.

booster

booster which booster to use, can be gbtree or gblinear. Default: gbtree.

objective

objective specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below: + reg:linear linear regression (Default). + reg:logistic logistic regression. + binary:logistic logistic regression for binary classification. Output probability. + binary:logitraw logistic regression for binary classification, output score before logistic transformation. + num_class set the number of classes. To use only with multiclass objectives. + multi:softmax set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to num_class - 1. + multi:softprob same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class. + rank:pairwise set xgboost to do ranking task by minimizing the pairwise loss.

eta

eta control the learning rate: scale the contribution of each tree by a factor of 0 < eta < 1 when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for eta implies larger value for nrounds: low eta value means model more robust to overfitting but slower to compute. Default: 0.3

gamma

gamma minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.gamma minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.

max_depth

max_depth maximum depth of a tree. Default: 6

min_child_weight

min_child_weight minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1

subsample

subsample subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with eta and increase nrounds. Default: 1

colsample_bytree

colsample_bytree subsample ratio of columns when constructing each tree. Default: 1

...

other parameters to pass to params.

Value

A object xgb.Booster.prmdt with additional information to the model that allows to homogenize the results.

Note

the parameter information was taken from the original function xgb.train.

See Also

The internal function is from package xgb.train.

Examples

# Classification
data("iris")

n <- seq_len(nrow(iris))
.sample <- sample(n, length(n) * 0.75)
data.train <- iris[.sample,]
data.test <- iris[-.sample,]

modelo.xg <- train.xgboost(Species~., data.train, nrounds = 10, maximize = FALSE)
modelo.xg
prob <- predict(modelo.xg, data.test, type = "prob")
prob
prediccion <- predict(modelo.xg, data.test, type = "class")
prediccion

# Regression
len <- nrow(swiss)
sampl <- sample(x = 1:len,size = len*0.20,replace = FALSE)
ttesting <- swiss[sampl,]
ttraining <- swiss[-sampl,]
model.xgb <- train.xgboost(Infant.Mortality~.,ttraining, nrounds = 10, maximize = FALSE)
prediction <- predict(model.xgb, ttesting)
prediction