第四节课r语言四周

R语言第四周Boosting算法GBDTAdaboostXgboost

XGboost目前应用最多的boosting模型，各大比赛平台常驻冠军需要在linux或者mac上安装，windows安装非常麻烦

Xgboostexamplexgboost(DMatrix,max.depth=2,eta=1,nthread=2,nround=2,objective="binary:logistic")Dmatrix:xgboost的数据集。可以灵活进行配置模型保存于读取保存模型，便于下次使用调用xgb.save(bst,"xgboost.model")Xgb.load(“xgboost.model”)聚类分析选择特征标准化数据检测异常值计算距离选择聚类算法决定多少个类别获取最终方案展示最终效果评估聚类方案K-means无监督学习思想：1SelectKcentroids(Krowschosenatrandom).2Assigneachdatapointtoitsclosestcentroid.3Recalculatethecentroidsastheaverageofalldatapointsinathecentroidsarep-lengthmeanvectors,wherepisthenumberofvariables4Assigndatapointstotheirclosestcentroids.5Continuesteps3and4untiltheobservationsaren’treassignemumnumberofiterations(Ruses10asadefault)isreachedK-MEAN算法

实现NbClust确认聚类的个数KmeansThefunctionreturnstheclustermemberships,centroids,sumsofsquares

(within,between,total),andclustersizes.

K-MEDIODS平均数容易受到异常值影响MEDIODS更加健壮可以使用任意的距离测量方法思想ThePAMalgorithmisasfollows:1RandomlyselectKobservations(calleachamedoid).2Calculatethedistance/dissimilarityofeveryobservationtoeachmedoid.3Assigneachobservationtoitsclosestmedoid.4Calculatethesumofthedistancesofeachobservationfromitsmedoid(totalcost).5Selectapointthatisn’tamedoid,andswapitwithitsmedoid.6Reassigneverypointtoitsclosestmedoid.7Calculatethetotalcost.8Ifthistotalcostissmaller,keepthenewpointasamedoid.9Repeatstep