xgboost GridSearchCV take too long or does not goes to the next step
$begingroup$
just strange
%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
max_depth=7, tree_method='gpu_exact')
this code takes around Wall time: 866 ms.
but when I do the gridsearchCV it does not goes to the next step
even though I gave only one parameter
%%time
xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
kfold = StratifiedKFold(n_splits=10, random_state=0)
xgb_param_grid = {
'learning_rate': [0.08,0.09],
'random_state': [0],
'max_depth': [8,9],
'n_estimators': [400,500]
}
xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
xgbGrid.fit(X,y)
xgb_best = xgbGrid.best_estimator_
for my understanding, this should not take that long.<br/>
it d
does not go to the next step I do not sure this even working or not
it stop with
Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.
data set size is (15035, 22)
am I doing something wrong?
machine-learning xgboost kaggle grid-search gridsearchcv
asked 10 mins ago
monkmonk
164
$endgroup$
add a comment |
$begingroup$
just strange
%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
max_depth=7, tree_method='gpu_exact')
this code takes around Wall time: 866 ms.
but when I do the gridsearchCV it does not goes to the next step
even though I gave only one parameter
%%time
xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
kfold = StratifiedKFold(n_splits=10, random_state=0)
xgb_param_grid = {
'learning_rate': [0.08,0.09],
'random_state': [0],
'max_depth': [8,9],
'n_estimators': [400,500]
}
xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
xgbGrid.fit(X,y)
xgb_best = xgbGrid.best_estimator_
for my understanding, this should not take that long.<br/>
it d
does not go to the next step I do not sure this even working or not
it stop with
Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.
data set size is (15035, 22)
am I doing something wrong?
machine-learning xgboost kaggle grid-search gridsearchcv
asked 10 mins ago
monkmonk
164
$endgroup$
add a comment |
$begingroup$
just strange
%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
max_depth=7, tree_method='gpu_exact')
this code takes around Wall time: 866 ms.
but when I do the gridsearchCV it does not goes to the next step
even though I gave only one parameter
%%time
xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
kfold = StratifiedKFold(n_splits=10, random_state=0)
xgb_param_grid = {
'learning_rate': [0.08,0.09],
'random_state': [0],
'max_depth': [8,9],
'n_estimators': [400,500]
}
xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
xgbGrid.fit(X,y)
xgb_best = xgbGrid.best_estimator_
for my understanding, this should not take that long.<br/>
it d
does not go to the next step I do not sure this even working or not
it stop with
Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.
data set size is (15035, 22)
am I doing something wrong?
machine-learning xgboost kaggle grid-search gridsearchcv
asked 10 mins ago
monkmonk
164
$endgroup$
just strange
%%time
xgb = xgb.XGBRegressor(n_estimators=500, learning_rate=0.07, gamma=0, subsample=0.75, colsample_bytree=1,
max_depth=7, tree_method='gpu_exact')
this code takes around Wall time: 866 ms.
but when I do the gridsearchCV it does not goes to the next step
even though I gave only one parameter
%%time
xgb = XGBClassifier(tree_method='gpu_exact',verbose_eval=True, silence=False)
kfold = StratifiedKFold(n_splits=10, random_state=0)
xgb_param_grid = {
'learning_rate': [0.08,0.09],
'random_state': [0],
'max_depth': [8,9],
'n_estimators': [400,500]
}
xgbGrid = gsRFC = GridSearchCV(xgb,param_grid = xgb_param_grid, cv=5, scoring="neg_mean_squared_error", n_jobs= 10, verbose = 1)
xgbGrid.fit(X,y)
xgb_best = xgbGrid.best_estimator_
for my understanding, this should not take that long.<br/>
it d
does not go to the next step I do not sure this even working or not
it stop with
Fitting 5 folds for each of 8 candidates, totalling 40 fits
[Parallel(n_jobs=10)]: Using backend LokyBackend with 10 concurrent
workers.
data set size is (15035, 22)
am I doing something wrong?
machine-learning xgboost kaggle grid-search gridsearchcv
machine-learning xgboost kaggle grid-search gridsearchcv
asked 10 mins ago
monkmonk
164
asked 10 mins ago
monkmonk
164
asked 10 mins ago
monkmonk
164
asked 10 mins ago
monkmonk
164
asked 10 mins ago
monkmonk
164
164
add a comment |
add a comment |
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