xgboost function for a simpler interface.
xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL, feval = NULL, verbose = 1, print.every.n = 1L, early.stop.round = NULL, maximize = NULL, ...)1. General Parameters
booster which booster to use, can be gbtree or gblinear. Default: gbtree
silent 0 means printing running messages, 1 means silent mode. Default: 0
2. Booster Parameters
2.1. Parameter for Tree Booster
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 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 maximum depth of a tree. Default: 6
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 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 nround. Default: 1
colsample_bytree subsample ratio of columns when constructing each tree. Default: 1
num_parallel_tree Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set colsample_bytree < 1, subsample < 1 and round = 1) accordingly. Default: 1
2.2. Parameter for Linear Booster
lambda L2 regularization term on weights. Default: 0
lambda_bias L2 regularization term on bias. Default: 0
alpha L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0
3. Task Parameters
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 tonum_class.
multi:softprob same as softmax, but output a vector of ndata * nclass, 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.
base_score the initial prediction score of all instances, global bias. Default: 0.5
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.
xgb.DMatrix as the input.verbose=1 or
verbose=2. Watchlist is used to specify validation set monitoring
during training. For example user can specify
watchlist=list(validation1=mat1, validation2=mat2) to watch
the performance of each round's model on mat1 and mat2list(metric='metric-name', value='metric-value') with given
prediction and dtrain,verbose>0. Default is 1 which means all messages are printed.NULL, the early stopping function is not triggered.
If set to an integer k, training with a validation set will stop if the performance
keeps getting worse consecutively for k rounds.feval and early.stop.round are set, then maximize must be set as well.
maximize=TRUE means the larger the evaluation score the better.params.xgboost.It supports advanced features such as watchlist, customized objective function (feval),
therefore it is more flexible than xgboost function.
Parallelization is automatically enabled if OpenMP is present.
Number of threads can also be manually specified via nthread parameter.
eval_metric parameter (not listed above) is set automatically by Xgboost but can be overriden by parameter. Below is provided the list of different metric optimized by Xgboost to help you to understand how it works inside or to use them with the watchlist parameter.
rmse root mean square error. http://en.wikipedia.org/wiki/Root_mean_square_error
logloss negative log-likelihood. http://en.wikipedia.org/wiki/Log-likelihood
error Binary classification error rate. It is calculated as (wrong cases) / (all cases). For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
merror Multiclass classification error rate. It is calculated as (wrong cases) / (all cases).
auc Area under the curve. http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve for ranking evaluation.
ndcg Normalized Discounted Cumulative Gain (for ranking task). http://en.wikipedia.org/wiki/NDCG
Full list of parameters is available in the Wiki https://github.com/dmlc/xgboost/wiki/Parameters.
This function only accepts an xgb.DMatrix object as the input.
data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- dtrain
watchlist <- list(eval = dtest, train = dtrain)
logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
preds <- 1/(1 + exp(-preds))
grad <- preds - labels
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
evalerror <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
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