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Hyper parameters tuning XGBClassifier

0

$begingroup$

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

share

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 | 

0

$begingroup$

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

share

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

share

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.

$endgroup$

train['outcome'].value_counts()

0 159730 
1 6843

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)

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_))

share

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.

share

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