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0

$begingroup$

I am trying to run a GRIDSEARCHCV (sklearn) on XGBRegressor. Documentation on the parameter says that if regression, then objective = reg:squarederror.(see https://github.com/dmlc/xgboost/tree/master/demo/regression) However, whenever I am trying to run the search, I am getting an error saying XGBoostError: b'[13:39:54] src/objective/objective.cc:23: Unknown objective function reg:squarederror.

I am not sure how to get around this problem. For the sake of completeness, below is the piece of code I am using for this purpose.

cv_params = 
 'n_estimators' : np.arange(100, 1201, 100),
 'max_depth' : np.arange(2, 10)
 

xgbr_params = 'objective':'reg:squarederror','n_jobs':-1,'random_state':4444,'min_child_weight':1,
 'eta':0.3,'subsample':0.8,'gamma':0.5,'colsample_bytree':0.8


opt_xgbr = GridSearchCV(xgb.XGBRegressor(**xgbr_params)
 ,param_grid=cv_params,scoring='r2',cv=5,n_jobs=-1,return_train_score=True, verbose=3)

Any help would be greatly appreciated.

Thanks

python scikit-learn xgboost hyperparameter-tuning gridsearchcv

share|improve this question

asked Apr 9 at 20:51

user62198user62198

5002619

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What version of xgboost are you using? import xgboost as xgb; xgb.__version__
  $endgroup$
  – TitoOrt
  Apr 10 at 7:25
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  >>>` import xgboost as xgb;xgb.__version__ '0.82'`. I thought this is the latest version. Am I right?
  $endgroup$
  – user62198
  Apr 10 at 15:45
  

  
  
  
  

add a comment | 

0

$begingroup$

I am trying to run a GRIDSEARCHCV (sklearn) on XGBRegressor. Documentation on the parameter says that if regression, then objective = reg:squarederror.(see https://github.com/dmlc/xgboost/tree/master/demo/regression) However, whenever I am trying to run the search, I am getting an error saying XGBoostError: b'[13:39:54] src/objective/objective.cc:23: Unknown objective function reg:squarederror.

I am not sure how to get around this problem. For the sake of completeness, below is the piece of code I am using for this purpose.

cv_params = 
 'n_estimators' : np.arange(100, 1201, 100),
 'max_depth' : np.arange(2, 10)
 

xgbr_params = 'objective':'reg:squarederror','n_jobs':-1,'random_state':4444,'min_child_weight':1,
 'eta':0.3,'subsample':0.8,'gamma':0.5,'colsample_bytree':0.8


opt_xgbr = GridSearchCV(xgb.XGBRegressor(**xgbr_params)
 ,param_grid=cv_params,scoring='r2',cv=5,n_jobs=-1,return_train_score=True, verbose=3)

Any help would be greatly appreciated.

Thanks

python scikit-learn xgboost hyperparameter-tuning gridsearchcv

share|improve this question

asked Apr 9 at 20:51

user62198user62198

5002619

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  What version of xgboost are you using? import xgboost as xgb; xgb.__version__
  $endgroup$
  – TitoOrt
  Apr 10 at 7:25
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  >>>` import xgboost as xgb;xgb.__version__ '0.82'`. I thought this is the latest version. Am I right?
  $endgroup$
  – user62198
  Apr 10 at 15:45
  

  
  
  
  

add a comment | 

$begingroup$

I am trying to run a GRIDSEARCHCV (sklearn) on XGBRegressor. Documentation on the parameter says that if regression, then objective = reg:squarederror.(see https://github.com/dmlc/xgboost/tree/master/demo/regression) However, whenever I am trying to run the search, I am getting an error saying XGBoostError: b'[13:39:54] src/objective/objective.cc:23: Unknown objective function reg:squarederror.

I am not sure how to get around this problem. For the sake of completeness, below is the piece of code I am using for this purpose.

cv_params = 
 'n_estimators' : np.arange(100, 1201, 100),
 'max_depth' : np.arange(2, 10)
 

xgbr_params = 'objective':'reg:squarederror','n_jobs':-1,'random_state':4444,'min_child_weight':1,
 'eta':0.3,'subsample':0.8,'gamma':0.5,'colsample_bytree':0.8


opt_xgbr = GridSearchCV(xgb.XGBRegressor(**xgbr_params)
 ,param_grid=cv_params,scoring='r2',cv=5,n_jobs=-1,return_train_score=True, verbose=3)

Any help would be greatly appreciated.

Thanks

python scikit-learn xgboost hyperparameter-tuning gridsearchcv

share|improve this question

asked Apr 9 at 20:51

user62198user62198

5002619

$endgroup$

cv_params = 
 'n_estimators' : np.arange(100, 1201, 100),
 'max_depth' : np.arange(2, 10)
 

xgbr_params = 'objective':'reg:squarederror','n_jobs':-1,'random_state':4444,'min_child_weight':1,
 'eta':0.3,'subsample':0.8,'gamma':0.5,'colsample_bytree':0.8


opt_xgbr = GridSearchCV(xgb.XGBRegressor(**xgbr_params)
 ,param_grid=cv_params,scoring='r2',cv=5,n_jobs=-1,return_train_score=True, verbose=3)

share|improve this question

asked Apr 9 at 20:51

user62198user62198

5002619

share|improve this question

asked Apr 9 at 20:51

user62198user62198

5002619

asked Apr 9 at 20:51

user62198user62198

5002619

asked Apr 9 at 20:51

user62198user62198

5002619