L1 regularization term on weights, increase this value will make model more conservative.
L1 regularization term on weights, increase this value will make model more conservative. [default=0]
the initial prediction score of all instances, global bias.
the initial prediction score of all instances, global bias. default=0.5
Booster to use, options: {'gbtree', 'gblinear', 'dart'}
Booster to use, options: {'gbtree', 'gblinear', 'dart'}
subsample ratio of columns for each split, in each level.
subsample ratio of columns for each split, in each level. [default=1] range: (0,1]
subsample ratio of columns when constructing each tree.
subsample ratio of columns when constructing each tree. [default=1] range: (0,1]
customized evaluation function provided by user.
customized evaluation function provided by user. default: null
customized objective function provided by user.
customized objective function provided by user. default: null
step size shrinkage used in update to prevents overfitting.
step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features and eta actually shrinks the feature weights to make the boosting process more conservative. [default=0.3] range: [0,1]
evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).
evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, ndcg, map, gamma-deviance
Explains all params of this instance.
Explains all params of this instance. See explainParam().
minimum loss reduction required to make a further partition on a leaf node of the tree.
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. [default=0] range: [0, Double.MaxValue]
L2 regularization term on weights, increase this value will make model more conservative.
L2 regularization term on weights, increase this value will make model more conservative. [default=1]
Parameter of linear booster L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
Parameter of linear booster L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
Maximum delta step we allow each tree's weight estimation to be.
Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update. [default=0] range: [0, Double.MaxValue]
maximum depth of a tree, increase this value will make model more complex / likely to be overfitting.
maximum depth of a tree, increase this value will make model more complex / likely to be overfitting. [default=6] range: [1, Int.MaxValue]
minimum sum of instance weight(hessian) needed in a child.
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] range: [0, Double.MaxValue]
the value treated as missing.
the value treated as missing. default: Float.NaN
number of workers used to train xgboost model.
number of workers used to train xgboost model. default: 1
Parameter of Dart booster.
Parameter of Dart booster. type of normalization algorithm, options: {'tree', 'forest'}. [default="tree"]
number of threads used by per worker.
number of threads used by per worker. default 1
Specify the learning task and the corresponding learning objective.
Specify the learning task and the corresponding learning objective. options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear
Parameter of Dart booster.
Parameter of Dart booster. dropout rate. [default=0.0] range: [0.0, 1.0]
The number of rounds for boosting
The number of rounds for boosting
Parameter for Dart booster.
Parameter for Dart booster. Type of sampling algorithm. "uniform": dropped trees are selected uniformly. "weighted": dropped trees are selected in proportion to weight. [default="uniform"]
Control the balance of positive and negative weights, useful for unbalanced classes.
Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). [default=0]
0 means printing running messages, 1 means silent mode.
0 means printing running messages, 1 means silent mode. default: 0
This is only used for approximate greedy algorithm.
This is only used for approximate greedy algorithm. This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy. [default=0.03] range: (0, 1)
Parameter of Dart booster.
Parameter of Dart booster. probability of skip dropout. If a dropout is skipped, new trees are added in the same manner as gbtree. [default=0.0] range: [0.0, 1.0]
subsample ratio of the training instance.
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. [default=1] range:(0,1]
produce a XGBoostModel by fitting the given dataset
produce a XGBoostModel by fitting the given dataset
The tree construction algorithm used in XGBoost.
The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'} [default='auto']
whether to use external memory as cache.
whether to use external memory as cache. default: false
XGBoost Estimator to produce a XGBoost model