XGBoost 参数
在运行 XGBoost 之前, 我们必须设置三种类型的参数: 常规参数, 提升器参数和任务参数.
 常规参数与我们用于提升的提升器有关，通常是树模型或线性模型
 提升器参数取决于你所选择的提升器
 学习任务的参数决定了学习场景, 例如回归任务可以使用不同的参数进行排序相关的任务
 命令行参数的行为与 xgboost 的 CLI 版本相关
R 中的参数
在 R 包中. 您可以使用 .（点）替换参数中下划线, 例如, 可以使用 max.depth 作为 max_depth. 下划线参数在 R 中也是有效的.
常规参数
 booster [default=gbtree]
 which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.
 silent [default=0]
 0 means printing running messages, 1 means silent mode.
 nthread [default to maximum number of threads available if not set]
 number of parallel threads used to run xgboost
 num_pbuffer [set automatically by xgboost, no need to be set by user]
 size of prediction buffer, normally set to number of training instances. The buffers are used to save the prediction results of last boosting step.
 num_feature [set automatically by xgboost, no need to be set by user]
 feature dimension used in boosting, set to maximum dimension of the feature
用于 Tree 提升的参数
 eta [default=0.3]
 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.
 range: [0,1]
 gamma [default=0]
 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.
 range: [0,∞]
 max_depth [default=6]
 maximum depth of a tree, increase this value will make model more complex / likely to be overfitting.
 range: [1,∞]
 min_child_weight [default=1]
 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.
 range: [0,∞]
 max_delta_step [default=0]
 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 110 might help control the update
 range: [0,∞]
 subsample [default=1]
 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.
 range: (0,1]
 colsample_bytree [default=1]
 subsample ratio of columns when constructing each tree.
 range: (0,1]
 colsample_bylevel [default=1]
 subsample ratio of columns for each split, in each level.
 range: (0,1]
 lambda [default=1]
 L2 regularization term on weights, increase this value will make model more conservative.
 alpha [default=0]
 L1 regularization term on weights, increase this value will make model more conservative.
 tree_method, string [default=’auto’]
 The tree constructtion algorithm used in XGBoost(see description in the reference paper)
 Distributed and external memory version only support approximate algorithm.
 Choices: {‘auto’, ‘exact’, ‘approx’}
 ‘auto’: Use heuristic to choose faster one.
 For small to medium dataset, exact greedy will be used.
 For very largedataset, approximate algorithm will be choosed.
 Because old behavior is always use exact greedy in single machine, user will get a message when approximate algorithm is choosed to notify this choice.
 ‘exact’: Exact greedy algorithm.
 ‘approx’: Approximate greedy algorithm using sketching and histogram.
 ‘auto’: Use heuristic to choose faster one.
 sketch_eps, [default=0.03]
 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 ganrantee with sketch accuracy.  Usuaully user do not have to tune this. but consider set to lower number for more accurate enumeration.
 range: (0, 1)
 scale_pos_weight, [default=0]
 Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See Parameters Tuning for more discussion. Also see Higgs Kaggle competition demo for examples: R, py1, py2, py3
用于 Dart Booster 的其它参数
 sample_type [default=”uniform”]
 type of sampling algorithm.
 “uniform”: dropped trees are selected uniformly.
 “weighted”: dropped trees are selected in proportion to weight.
 type of sampling algorithm.
 normalize_type [default=”tree”]
 type of normalization algorithm.
 “tree”: new trees have the same weight of each of dropped trees.
 weight of new trees are 1 / (k + learnig_rate)
 dropped trees are scaled by a factor of k / (k + learning_rate)
 “forest”: new trees have the same weight of sum of dropped trees (forest).
 weight of new trees are 1 / (1 + learning_rate)
 dropped trees are scaled by a factor of 1 / (1 + learning_rate)
 “tree”: new trees have the same weight of each of dropped trees.
 type of normalization algorithm.
 rate_drop [default=0.0]
 dropout rate.
 range: [0.0, 1.0]
 skip_drop [default=0.0]
 probability of skip dropout.
 If a dropout is skipped, new trees are added in the same manner as gbtree.
 range: [0.0, 1.0]
 probability of skip dropout.
用于 Linear Booster 的参数
 lambda [default=0]
 L2 regularization term on weights, increase this value will make model more conservative.
 alpha [default=0]
 L1 regularization term on weights, increase this value will make model more conservative.
 lambda_bias
 L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)
学习任务的参数
Specify the learning task and the corresponding learning objective. The objective options are below:

objective [ default=reg:linear ]

“reg:linear” –linear regression
 “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
 “count:poisson” –poisson regression for count data, output mean of poisson distribution
 max_delta_step is set to 0.7 by default in poisson regression (used to safeguard optimization)
 “multi:softmax” –set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
 “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 probability of each data point belonging to each class.
 “rank:pairwise” –set XGBoost to do ranking task by minimizing the pairwise loss

“reg:gamma” –gamma regression for severity data, output mean of gamma distribution

base_score [ default=0.5 ]
 the initial prediction score of all instances, global bias
 for sufficent number of iterations, changing this value will not have too much effect.
 eval_metric [ default according to objective ]
 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 )
 User can add multiple evaluation metrics, for python user, remember to pass the metrics in as list of parameters pairs instead of map, so that latter ‘eval_metric’ won’t override previous one
 The choices are listed below:
 “rmse”: root mean square error
 “mae”: mean absolute error
 “logloss”: negative loglikelihood
 “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).
 “mlogloss”: Multiclass logloss
 “auc”: Area under the curve for ranking evaluation.
 “ndcg”:Normalized Discounted Cumulative Gain
 “map”:Mean average precision
 “ndcg@n”,”map@n”: n can be assigned as an integer to cut off the top positions in the lists for evaluation.
 “ndcg”,”map”,”ndcg@n”,”map@n”: In XGBoost, NDCG and MAP will evaluate the score of a list without any positive samples as 1. By adding “” in the evaluation metric XGBoost will evaluate these score as 0 to be consistent under some conditions. training repeatively
 “gammadeviance”: [residual deviance for gamma regression]

seed [ default=0 ]

random number seed.
命令行参数
The following parameters are only used in the console version of xgboost

use_buffer [ default=1 ]

Whether to create a binary buffer from text input. Doing so normally will speed up loading times

num_round

The number of rounds for boosting

data
 The path of training data
 test:data
 The path of test data to do prediction

save_period [default=0]

the period to save the model, setting save_period=10 means that for every 10 rounds XGBoost will save the model, setting it to 0 means not save any model during training.

task [default=train] options: train, pred, eval, dump
 train: training using data
 pred: making prediction for test:data
 eval: for evaluating statistics specified by eval[name]=filename
 dump: for dump the learned model into text format(preliminary)
 model_in [default=NULL]
 path to input model, needed for test, eval, dump, if it is specified in training, xgboost will continue training from the input model
 model_out [default=NULL]
 path to output model after training finishes, if not specified, will output like 0003.model where 0003 is number of rounds to do boosting.
 model_dir [default=models]
 The output directory of the saved models during training
 fmap
 feature map, used for dump model
 name_dump [default=dump.txt]
 name of model dump file
 name_pred [default=pred.txt]
 name of prediction file, used in pred mode
 pred_margin [default=0]
 predict margin instead of transformed probability