对于一个算法工程师而言，xgboost应该算的上是起手式，网上也有各式各样的教程，这篇博客主要从原理、代码、调参和上线进行覆盖，进而构建一个直观的算法体系；
生成的二叉树是满二叉树还是完全二叉树？
调参方法
param = {
# step size
'eta': 0.1,
# model param, the weight value of the number of leaves, larger -> under fitting
# 'gamma': 0.1,
'max_depth': depth,
# pruning param, min instance weight in hessian needed in a child; larger -> under fitting
# 'min_child_weight': 1,
# pruning param, update constraint, larger -> under fitting
# 'max_delta_step': 0,
'subsample': 0.8,
# column sample ratio each tree
# 'colsample_bytree': 0.8,
# column sample ratio each layer
'colsample_bylevel': 0.3,
# L2 regularization term
# 'lambda': 1,
# L1 regularization term
'alpha': 0.1,
# small data set -> exact, large data set -> approx, just choose auto
# 'tree_method': 'auto',
# 'sketch_eps': 0.03,
# for unbalanced data set, pos: value, neg: 1
# 'scale_pos_weight': 1 / weight,
# "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
'objective': 'binary:logistic',
# threshold
'base_score': weight / (weight + 1),
# "rmse": root mean square error
# "mae": mean absolute error
# "logloss": negative 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).
# "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 repeatedly
# "gamma-deviance": [residual deviance for gamma regression]
'eval_metric': 'auc',
'seed': 31,
}
上线实录：
在实际生产过程中，难免遇到将模型上线的问题，这里将流程进行拆解；
1.模型训练完成—保存模型文件—解析模型文件—重写代码读取解析文件预测树；
Xgb的损失函数的作用；
Java解析源码，具体的代码和注释已经更新在github上，