1、原理
https://www.cnblogs.com/zhouxiaohui888/p/6008368.html
2、实战
xgboost中比较重要的参数介绍:
(1)学习率:learning rate :一般设置比较低,0.1以下
(2)tree:
max_depth
min_child_weight
subsample
colsample_bytree
gamma
(3)正则化参数
lambda
alpha
(1)objective [ default=reg:linear ] 定义学习任务及相应的学习目标,可选的目标函数如下:
- “reg:linear” –线性回归。
- “reg:logistic” –逻辑回归。
- “binary:logistic” –二分类的逻辑回归问题,输出为概率。
- “binary:logitraw” –二分类的逻辑回归问题,输出的结果为wTx。
- “count:poisson” –计数问题的poisson回归,输出结果为poisson分布。 在poisson回归中,max_delta_step的缺省值为0.7。(used to safeguard optimization)
- “multi:softmax” –让XGBoost采用softmax目标函数处理多分类问题,同时需要设置参数num_class(类别个数)
- “multi:softprob” –和softmax一样,但是输出的是ndata * nclass的向量,可以将该向量reshape成ndata行nclass列的矩阵。没行数据表示样本所属于每个类别的概率。
- “rank:pairwise” –set XGBoost to do ranking task by minimizing the pairwise loss
(2)’eval_metric’ The choices are listed below,评估指标:
- “rmse”: root mean square 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.
(3)lambda [default=0] L2 正则的惩罚系数
(4)alpha [default=0] L1 正则的惩罚系数
(5)lambda_bias 在偏置上的L2正则。缺省值为0(在L1上没有偏置项的正则,因为L1时偏置不重要)
(6)eta [default=0.3]
为了防止过拟合,更新过程中用到的收缩步长。在每次提升计算之后,算法会直接获得新特征的权重。 eta通过缩减特征的权重使提升计算过程更加保守。缺省值为0.3
取值范围为:[0,1]
(7)max_depth [default=6] 数的最大深度。缺省值为6 ,取值范围为:[1,∞]
(8)min_child_weight [default=1]
孩子节点中最小的样本权重和。如果一个叶子节点的样本权重和小于min_child_weight则拆分过程结束。在现行回归模型中,这个参数是指建立每个模型所需要的最小样本数。该成熟越大算法越conservative
取值范围为: [0,∞]
xgb1=XGBClassifier( learning_rate=0.1, n_estimators=1000, max_depth=5, min_child_weight=1, gamma=0, subsample=0.8 colsample_bytree=0.8, objective='binary:logistic', nthread=4, scale_pos_weight=1, seed=27)
3、xgboost重要模块:plot_importance【显示特征的重要性】
from xgboost import XGBClassifier from xgboost import plot_importance from matplotlib import pyplot model=XGBClassifier() model.fit(X,Y) plot_importance(model) pyplot.show() #图中就可以显示出各种特征的重要性