XGBoostClassificationModel
Related Docs:
object XGBoostClassificationModel
| package spark
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]
Param for initial prediction (aka base margin) column name.
Param for initial prediction (aka base margin) column name.
the initial prediction score of all instances, global bias.
the initial prediction score of all instances, global bias. default=0.5
Param for set checkpoint interval (>= 1) or disable checkpoint (-1).
Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that
the trained model will get checkpointed every 10 iterations. Note: checkpoint_path must
also be set if the checkpoint interval is greater than 0.
The hdfs folder to load and save checkpoint boosters.
The hdfs folder to load and save checkpoint boosters. default: empty_string
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]
Param for contribution prediction column name.
Param for contribution prediction column name.
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, aucpr, ndcg, map, gamma-deviance
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]
growth policy for fast histogram algorithm
growth policy for fast histogram algorithm
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)
Param for leaf prediction column name.
Param for leaf prediction column name.
maximum number of bins in histogram
maximum number of bins in histogram
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
Get the native booster instance of this model.
Get the native booster instance of this model. This is used to call low-level APIs on native booster, such as "getFeatureScore".
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
number of classes
number of classes
If non-zero, the training will be stopped after a specified number of consecutive increases in any evaluation metric.
If non-zero, the training will be stopped after a specified number of consecutive increases in any evaluation metric.
The number of rounds for boosting
The number of rounds for boosting
number of workers used to train xgboost model.
number of workers used to train xgboost model. 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
Single instance prediction.
Single instance prediction. Note: The performance is not ideal, use it carefully!
Parameter of Dart booster.
Parameter of Dart booster. dropout rate. [default=0.0] range: [0.0, 1.0]
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=1]
Random seed for the C++ part of XGBoost and train/test splitting.
Random seed for the C++ part of XGBoost and train/test splitting.
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]
Returns summary (e.g.
Returns summary (e.g. train/test objective history) of model on the training set. An exception is thrown if no summary is available.
the maximum time to wait for the job requesting new workers.
the maximum time to wait for the job requesting new workers. default: 30 minutes
Rabit tracker configurations.
Rabit tracker configurations. The parameter must be provided as an instance of the TrackerConf class, which has the following definition:
case class TrackerConf(workerConnectionTimeout: Duration, trainingTimeout: Duration, trackerImpl: String)
See below for detailed explanations.
Choice between "python" or "scala". The former utilizes the Java wrapper of the Python Rabit tracker (in dmlc_core), and does not support timeout settings. The "scala" version removes Python components, and fully supports timeout settings.
The timeout value should take the time of data loading and pre-processing into account, due to the lazy execution of Spark's operations. Alternatively, you may force Spark to perform data transformation before calling XGBoost.train(), so that this timeout truly reflects the connection delay. Set a reasonable timeout value to prevent model training/testing from hanging indefinitely, possible due to network issues. Note that zero timeout value means to wait indefinitely (equivalent to Duration.Inf). Ignored if the tracker implementation is "python".
Fraction of training points to use for testing.
Fraction of training points to use for testing.
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