How to implement gridsearchCV for onevsrestclassifier of LogisticRegression classifier?
0
$begingroup$
parameters = [{'C': [10**-2, 10**-1, 10**0,10**1, 10**2, 10**3]}]
model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
model_tunning.fit(x_train_multilabel, y_train)
ValueError Traceback (most recent call last)
<ipython-input-38-5d5850fe8978> in <module>()
2
3 model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
----> 4 model_tunning.fit(x_train_multilabel, y_train)
ValueError: Invalid parameter C for estimator OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l1', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1). Check the list of available parameters with `estimator.get_params().keys()
logistic-regression
edited Nov 26 '18 at 12:50
ebrahimi
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
$endgroup$
bumped to the homepage by Community♦ 32 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
0
$begingroup$
parameters = [{'C': [10**-2, 10**-1, 10**0,10**1, 10**2, 10**3]}]
model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
model_tunning.fit(x_train_multilabel, y_train)
ValueError Traceback (most recent call last)
<ipython-input-38-5d5850fe8978> in <module>()
2
3 model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
----> 4 model_tunning.fit(x_train_multilabel, y_train)
ValueError: Invalid parameter C for estimator OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l1', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1). Check the list of available parameters with `estimator.get_params().keys()
logistic-regression
edited Nov 26 '18 at 12:50
ebrahimi
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
$endgroup$
bumped to the homepage by Community♦ 32 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
0
0
0
$begingroup$
parameters = [{'C': [10**-2, 10**-1, 10**0,10**1, 10**2, 10**3]}]
model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
model_tunning.fit(x_train_multilabel, y_train)
ValueError Traceback (most recent call last)
<ipython-input-38-5d5850fe8978> in <module>()
2
3 model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
----> 4 model_tunning.fit(x_train_multilabel, y_train)
ValueError: Invalid parameter C for estimator OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l1', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1). Check the list of available parameters with `estimator.get_params().keys()
logistic-regression
edited Nov 26 '18 at 12:50
ebrahimi
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
$endgroup$
parameters = [{'C': [10**-2, 10**-1, 10**0,10**1, 10**2, 10**3]}]
model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
model_tunning.fit(x_train_multilabel, y_train)
ValueError Traceback (most recent call last)
<ipython-input-38-5d5850fe8978> in <module>()
2
3 model_tunning = GridSearchCV(OneVsRestClassifier(LogisticRegression(penalty='l1')), param_grid=parameters,scoring="f1")
----> 4 model_tunning.fit(x_train_multilabel, y_train)
ValueError: Invalid parameter C for estimator OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
penalty='l1', random_state=None, solver='liblinear', tol=0.0001,
verbose=0, warm_start=False),
n_jobs=1). Check the list of available parameters with `estimator.get_params().keys()
logistic-regression
logistic-regression
edited Nov 26 '18 at 12:50
ebrahimi
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
edited Nov 26 '18 at 12:50
ebrahimi
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
edited Nov 26 '18 at 12:50
ebrahimi
7282821
edited Nov 26 '18 at 12:50
ebrahimi
7282821
edited Nov 26 '18 at 12:50
ebrahimi
7282821
7282821
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
asked Nov 25 '18 at 16:39
Satyam KumarSatyam Kumar
11
11
bumped to the homepage by Community♦ 32 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 32 secs ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the LogisticRegression model is stored as an attribute named estimator inside the OneVsRestClassifier model:
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
tuned_parameters = [{'estimator__C': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}]
# Find Optimal C by grid search
log_reg_clf = OneVsRestClassifier(LogisticRegression())
logistic_gs = GridSearchCV(log_reg_clf, tuned_parameters,scoring = 'f1_micro', cv=3)
logistic_gs.fit(x_train_bow, y_train)
print(logistic_gs.best_estimator_)
answered Jan 8 at 6:55
NishantNishant
1
$endgroup$
add a comment |
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1 Answer
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$begingroup$
When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the LogisticRegression model is stored as an attribute named estimator inside the OneVsRestClassifier model:
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
tuned_parameters = [{'estimator__C': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}]
# Find Optimal C by grid search
log_reg_clf = OneVsRestClassifier(LogisticRegression())
logistic_gs = GridSearchCV(log_reg_clf, tuned_parameters,scoring = 'f1_micro', cv=3)
logistic_gs.fit(x_train_bow, y_train)
print(logistic_gs.best_estimator_)
answered Jan 8 at 6:55
NishantNishant
1
$endgroup$
add a comment |
0
$begingroup$
When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the LogisticRegression model is stored as an attribute named estimator inside the OneVsRestClassifier model:
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
tuned_parameters = [{'estimator__C': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}]
# Find Optimal C by grid search
log_reg_clf = OneVsRestClassifier(LogisticRegression())
logistic_gs = GridSearchCV(log_reg_clf, tuned_parameters,scoring = 'f1_micro', cv=3)
logistic_gs.fit(x_train_bow, y_train)
print(logistic_gs.best_estimator_)
answered Jan 8 at 6:55
NishantNishant
1
$endgroup$
add a comment |
0
0
0
$begingroup$
When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the LogisticRegression model is stored as an attribute named estimator inside the OneVsRestClassifier model:
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
tuned_parameters = [{'estimator__C': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}]
# Find Optimal C by grid search
log_reg_clf = OneVsRestClassifier(LogisticRegression())
logistic_gs = GridSearchCV(log_reg_clf, tuned_parameters,scoring = 'f1_micro', cv=3)
logistic_gs.fit(x_train_bow, y_train)
print(logistic_gs.best_estimator_)
answered Jan 8 at 6:55
NishantNishant
1
$endgroup$
When you use nested estimators with grid search you can scope the parameters with __ as a separator. In this case the LogisticRegression model is stored as an attribute named estimator inside the OneVsRestClassifier model:
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
tuned_parameters = [{'estimator__C': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]}]
# Find Optimal C by grid search
log_reg_clf = OneVsRestClassifier(LogisticRegression())
logistic_gs = GridSearchCV(log_reg_clf, tuned_parameters,scoring = 'f1_micro', cv=3)
logistic_gs.fit(x_train_bow, y_train)
print(logistic_gs.best_estimator_)
answered Jan 8 at 6:55
NishantNishant
1
answered Jan 8 at 6:55
NishantNishant
1
answered Jan 8 at 6:55
NishantNishant
1
answered Jan 8 at 6:55
NishantNishant
1
1
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