importnumpyasnpimportxgboostasxgb#  this script demonstrates how to fit gamma regression model (with log link function)#  in xgboost, before running the demo you need to generate the autoclaims dataset#  by running gen_autoclaims.R located in xgboost/demo/data.data=np.genfromtxt('../data/autoclaims.csv',delimiter=',')dtrain=xgb.DMatrix(data[0:4741,0:34],data[0:4741,34])dtest=xgb.DMatrix(data[4741:6773,0:34],data[4741:6773,34])# for gamma regression, we need to set the objective to 'reg:gamma', it also suggests# to set the base_score to a value between 1 to 5 if the number of iteration is smallparam={'objective':'reg:gamma','booster':'gbtree','base_score':3}# the rest of settings are the samewatchlist=[(dtest,'eval'),(dtrain,'train')]num_round=30# training and evaluationbst=xgb.train(param,dtrain,num_round,watchlist)preds=bst.predict(dtest)labels=dtest.get_label()print('test deviance=%f'%(2*np.sum((labels-preds)/preds-np.log(labels)+np.log(preds))))

DownloadJupyternotebook:gamma_regression.ipynb

DownloadPythonsourcecode:gamma_regression.py

Downloadzipped:gamma_regression.zip