Hyper parameters tuning XGBClassifier Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsOverfitting XGBoostdifference between XGBRegressor and XGBClassifierWhich parameters are hyper parameters in a linear regression?Hyper parameters and ValidationSetA way to Identify tuning parameters and their possible rangeComparing XGBR with CatBoost performanceHyperparameter tuning for stacked modelsWhich is first ? Tuning the parameters or selecting the modelProblem about tuning hyper-parametresSetting best SVM hyper parameters
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Hyper parameters tuning XGBClassifier
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsOverfitting XGBoostdifference between XGBRegressor and XGBClassifierWhich parameters are hyper parameters in a linear regression?Hyper parameters and ValidationSetA way to Identify tuning parameters and their possible rangeComparing XGBR with CatBoost performanceHyperparameter tuning for stacked modelsWhich is first ? Tuning the parameters or selecting the modelProblem about tuning hyper-parametresSetting best SVM hyper parameters
$begingroup$
I am working on a highly imbalanced dataset for a competition.
The training data shape is : (166573, 14)
train['outcome'].value_counts()
0 159730
1 6843
I am using XGBClassifier for building model and the only parameter I manually set is scale_pos_weight : 23.34 (0 value counts / 1 value counts)
and it's giving around 82% under AUC metric.
I guess I can get much accuracy if I hypertune all other parameters.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=23.4, seed=None,
silent=True, subsample=1)
I tried GridSearchCV but it's taking a lot of time to complete on my local machine and I am not able to get any result back.
clf = XGBClassifier()
grid = GridSearchCV(clf,
params, n_jobs=-1,
scoring="roc_auc",
cv=3)
grid.fit(X_train, y_train)
print("Best: %f using %s" % (grid.best_score_, grid.best_params_))
What others parameters should I target to tune considering higly imbalanced dataset and how to run it so that I can actually get some results back?
xgboost cross-validation hyperparameter-tuning
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am working on a highly imbalanced dataset for a competition.
The training data shape is : (166573, 14)
train['outcome'].value_counts()
0 159730
1 6843
I am using XGBClassifier for building model and the only parameter I manually set is scale_pos_weight : 23.34 (0 value counts / 1 value counts)
and it's giving around 82% under AUC metric.
I guess I can get much accuracy if I hypertune all other parameters.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=23.4, seed=None,
silent=True, subsample=1)
I tried GridSearchCV but it's taking a lot of time to complete on my local machine and I am not able to get any result back.
clf = XGBClassifier()
grid = GridSearchCV(clf,
params, n_jobs=-1,
scoring="roc_auc",
cv=3)
grid.fit(X_train, y_train)
print("Best: %f using %s" % (grid.best_score_, grid.best_params_))
What others parameters should I target to tune considering higly imbalanced dataset and how to run it so that I can actually get some results back?
xgboost cross-validation hyperparameter-tuning
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I am working on a highly imbalanced dataset for a competition.
The training data shape is : (166573, 14)
train['outcome'].value_counts()
0 159730
1 6843
I am using XGBClassifier for building model and the only parameter I manually set is scale_pos_weight : 23.34 (0 value counts / 1 value counts)
and it's giving around 82% under AUC metric.
I guess I can get much accuracy if I hypertune all other parameters.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=23.4, seed=None,
silent=True, subsample=1)
I tried GridSearchCV but it's taking a lot of time to complete on my local machine and I am not able to get any result back.
clf = XGBClassifier()
grid = GridSearchCV(clf,
params, n_jobs=-1,
scoring="roc_auc",
cv=3)
grid.fit(X_train, y_train)
print("Best: %f using %s" % (grid.best_score_, grid.best_params_))
What others parameters should I target to tune considering higly imbalanced dataset and how to run it so that I can actually get some results back?
xgboost cross-validation hyperparameter-tuning
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I am working on a highly imbalanced dataset for a competition.
The training data shape is : (166573, 14)
train['outcome'].value_counts()
0 159730
1 6843
I am using XGBClassifier for building model and the only parameter I manually set is scale_pos_weight : 23.34 (0 value counts / 1 value counts)
and it's giving around 82% under AUC metric.
I guess I can get much accuracy if I hypertune all other parameters.
XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,
max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=23.4, seed=None,
silent=True, subsample=1)
I tried GridSearchCV but it's taking a lot of time to complete on my local machine and I am not able to get any result back.
clf = XGBClassifier()
grid = GridSearchCV(clf,
params, n_jobs=-1,
scoring="roc_auc",
cv=3)
grid.fit(X_train, y_train)
print("Best: %f using %s" % (grid.best_score_, grid.best_params_))
What others parameters should I target to tune considering higly imbalanced dataset and how to run it so that I can actually get some results back?
xgboost cross-validation hyperparameter-tuning
xgboost cross-validation hyperparameter-tuning
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
PraveenksPraveenks
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 6 mins ago
PraveenksPraveenks
1062
asked 6 mins ago
PraveenksPraveenks
1062
1062
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Praveenks is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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