Kaggle competition: Predicting Molecular Properties
DM_0=xgb.DMatrix(X_train_0,y_train_0.apply(lambda x:np.log1p(x)))
 params={"booster":"gbtree"}
xgbr=xgb.train(dtrain=DM_0,num_boost_round=380,params=params)
 pred = np.expm1(xgbr.predict(DM_1))
pred[pred<0]=0
print(mean_squared_log_error(y_train_1,pred)**0.5 )<train_model>
sub4 = pd.read_csv('.. /input/from-sub-lgb/sub_lgb_model_individual.csv')
 sub6 = pd.read_csv('.. /input/stealing/submission.csv')
 sub7 = pd.read_csv('.. /input/stealing/submission1.csv')
 print(sub4['scalar_coupling_constant'].describe())
 print(sub6['scalar_coupling_constant'].describe())
 print(sub7['scalar_coupling_constant'].describe())
params={"booster":"gbtree","learning_rate":0.09,"n_jobs":-1,"subsample":0.7,"alpha": 0.009}
xgbr=xgb.train(dtrain=DM_0,num_boost_round=380,params=params)
 pred = np.expm1(xgbr.predict(DM_1))
print(mean_squared_log_error(y_train_1,pred)**0.5 )<set_options>
sub6['scalar_coupling_constant'] = 0.34*sub6['scalar_coupling_constant'] + 0.33*sub7['scalar_coupling_constant'] + 0.33*sub4['scalar_coupling_constant']
sub6.to_csv('submission.csv', index=False )