第四節(jié)課r語言四周

R語言第四周Boosting算法GBDTAdaboostXgboost

XGboost目前應(yīng)用最多的boosting模型，各大比賽平臺常駐冠軍需要在linux或者mac上安裝，windows安裝非常麻煩

Xgboostexamplexgboost(DMatrix,max.depth=2,eta=1,nthread=2,nround=2,objective="binary:logistic")Dmatrix:xgboost的數(shù)據(jù)集?？梢造`活進(jìn)行配置模型保存于讀取保存模型，便于下次使用調(diào)用xgb.save(bst,"xgboost.model")Xgb.load(“xgboost.model”)聚類分析選擇特征標(biāo)準(zhǔn)化數(shù)據(jù)檢測異常值計(jì)算距離選擇聚類算法決定多少個(gè)類別獲取最終方案展示最終效果評估聚類方案K-means無監(jiān)督學(xué)習(xí)思想：1SelectKcentroids(Krowschosenatrandom).2Assigneachdatapointtoitsclosestcentroid.3Recalculatethecentroidsastheaverageofalldatapointsinathecentroidsarep-lengthmeanvectors,wherepisthenumberofvariables4Assigndatapointstotheirclosestcentroids.5Continuesteps3and4untiltheobservationsaren’treassignemumnumberofiterations(Ruses10asadefault)isreachedK-MEAN算法

實(shí)現(xiàn)NbClust確認(rèn)聚類的個(gè)數(shù)KmeansThefunctionreturnstheclustermemberships,centroids,sumsofsquares

(within,between,total),andclustersizes.

K-MEDIODS平均數(shù)容易受到異常值影響MEDIODS更加健壯可以使用任意的距離測量方法思想ThePAMalgorithmisasfollows:1RandomlyselectKobservations(calleachamedoid).2Calculatethedistance/dissimilarityofeveryobservationtoeachmedoid.3Assigneachobservationtoitsclosestmedoid.4Calculatethesumofthedistancesofeachobservationfromitsmedoid(totalcost).5Selectapointthatisn’tamedoid,andswapitwithitsmedoid.6Reassigneverypointtoitsclosestmedoid.7Calculatethetotalcost.8Ifthistotalcostissmaller,keepthenewpointasamedoid.9Repeatstep