XGBOOST (sklearn interface) REGRESSION error The 2019 Stack Overflow Developer Survey Results Are InXGBoost Linear Regression output incorrectWhy is xgboost so much faster than sklearn GradientBoostingClassifier?Sklearn regression problemXGBoost (R Interface) throwing “label set cannot be empty” errorLarge mean squared error in sklearn regressorsCustom objective function in xgboost for Regressionsklearn .fit errorComparing XGBR with CatBoost performanceImproving prediction accuracy with XGBoostXGBoost regression
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XGBOOST (sklearn interface) REGRESSION error
The 2019 Stack Overflow Developer Survey Results Are InXGBoost Linear Regression output incorrectWhy is xgboost so much faster than sklearn GradientBoostingClassifier?Sklearn regression problemXGBoost (R Interface) throwing “label set cannot be empty” errorLarge mean squared error in sklearn regressorsCustom objective function in xgboost for Regressionsklearn .fit errorComparing XGBR with CatBoost performanceImproving prediction accuracy with XGBoostXGBoost regression
$begingroup$
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
asked 9 hours ago
user62198user62198
4732617
$endgroup$
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
asked 9 hours ago
user62198user62198
4732617
$endgroup$
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
asked 9 hours ago
user62198user62198
4732617
$endgroup$
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
python scikit-learn xgboost hyperparameter-tuning gridsearchcv
asked 9 hours ago
user62198user62198
4732617
asked 9 hours ago
user62198user62198
4732617
asked 9 hours ago
user62198user62198
4732617
asked 9 hours ago
user62198user62198
4732617
asked 9 hours ago
user62198user62198
4732617
4732617
add a comment |
add a comment |
0
active
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