importosimportxgboostasxgb###  this script demonstrate how to fit generalized linear model in xgboost#  basically, we are using linear model, instead of tree for our boosters##CURRENT_DIR=os.path.dirname(__file__)dtrain=xgb.DMatrix(os.path.join(CURRENT_DIR,"../data/agaricus.txt.train?format=libsvm"))dtest=xgb.DMatrix(os.path.join(CURRENT_DIR,"../data/agaricus.txt.test?format=libsvm"))# change booster to gblinear, so that we are fitting a linear model# alpha is the L1 regularizer# lambda is the L2 regularizer# you can also set lambda_bias which is L2 regularizer on the bias termparam={"objective":"binary:logistic","booster":"gblinear","alpha":0.0001,"lambda":1,}# normally, you do not need to set eta (step_size)# XGBoost uses a parallel coordinate descent algorithm (shotgun),# there could be affection on convergence with parallelization on certain cases# setting eta to be smaller value, e.g 0.5 can make the optimization more stable# param['eta'] = 1### the rest of settings are the same##watchlist=[(dtest,"eval"),(dtrain,"train")]num_round=4bst=xgb.train(param,dtrain,num_round,watchlist)preds=bst.predict(dtest)labels=dtest.get_label()print("error=%f"%(sum(1foriinrange(len(preds))ifint(preds[i]>0.5)!=labels[i])/float(len(preds))))

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