ValueError: 'balanced_accuracy' is not a valid scoring value in scikit-learn
up vote
1
down vote
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I tried to pass to GridSearchCV other scoring metrics like balanced_accuracy for Binary Classification (instead of the default accuracy)
scoring = ['balanced_accuracy','recall','roc_auc','f1','precision']
validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv)
and got this error
ValueError: 'balanced_accuracy' is not a valid scoring value. Valid
options are
['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision','completeness_score','explained_variance','f1','f1_macro','f1_micro','f1_samples','f1_weighted','fowlkes_mallows_score','homogeneity_score','mutual_info_score','neg_log_loss','neg_mean_absolute_error','neg_mean_squared_error','neg_mean_squared_log_error','neg_median_absolute_error','normalized_mutual_info_score','precision','precision_macro','precision_micro','precision_samples','precision_weighted','r2','recall','recall_macro','recall_micro','recall_samples','recall_weighted','roc_auc','v_measure_score']
This is strange because 'balanced_accuracy' should be valid
Without defining balanced_accuracy then the code works fine
scoring = ['recall','roc_auc','f1','precision']
Also the scoring metrics in the error above seems to be different from the ones in the document
Any ideas why? Thank you so much
scikit-learn version is 0.19.2
python machine-learning scikit-learn metrics
asked Nov 17 at 7:54
Long
4418
add a comment |
up vote
1
down vote
favorite
I tried to pass to GridSearchCV other scoring metrics like balanced_accuracy for Binary Classification (instead of the default accuracy)
scoring = ['balanced_accuracy','recall','roc_auc','f1','precision']
validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv)
and got this error
ValueError: 'balanced_accuracy' is not a valid scoring value. Valid
options are
['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision','completeness_score','explained_variance','f1','f1_macro','f1_micro','f1_samples','f1_weighted','fowlkes_mallows_score','homogeneity_score','mutual_info_score','neg_log_loss','neg_mean_absolute_error','neg_mean_squared_error','neg_mean_squared_log_error','neg_median_absolute_error','normalized_mutual_info_score','precision','precision_macro','precision_micro','precision_samples','precision_weighted','r2','recall','recall_macro','recall_micro','recall_samples','recall_weighted','roc_auc','v_measure_score']
This is strange because 'balanced_accuracy' should be valid
Without defining balanced_accuracy then the code works fine
scoring = ['recall','roc_auc','f1','precision']
Also the scoring metrics in the error above seems to be different from the ones in the document
Any ideas why? Thank you so much
scikit-learn version is 0.19.2
python machine-learning scikit-learn metrics
asked Nov 17 at 7:54
Long
4418
3
Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it withsklearn.__version__))
– desertnaut
Nov 17 at 8:58
Thank you I added the code andscikit-learnversion
– Long
Nov 17 at 14:54
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I tried to pass to GridSearchCV other scoring metrics like balanced_accuracy for Binary Classification (instead of the default accuracy)
scoring = ['balanced_accuracy','recall','roc_auc','f1','precision']
validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv)
and got this error
ValueError: 'balanced_accuracy' is not a valid scoring value. Valid
options are
['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision','completeness_score','explained_variance','f1','f1_macro','f1_micro','f1_samples','f1_weighted','fowlkes_mallows_score','homogeneity_score','mutual_info_score','neg_log_loss','neg_mean_absolute_error','neg_mean_squared_error','neg_mean_squared_log_error','neg_median_absolute_error','normalized_mutual_info_score','precision','precision_macro','precision_micro','precision_samples','precision_weighted','r2','recall','recall_macro','recall_micro','recall_samples','recall_weighted','roc_auc','v_measure_score']
This is strange because 'balanced_accuracy' should be valid
Without defining balanced_accuracy then the code works fine
scoring = ['recall','roc_auc','f1','precision']
Also the scoring metrics in the error above seems to be different from the ones in the document
Any ideas why? Thank you so much
scikit-learn version is 0.19.2
python machine-learning scikit-learn metrics
asked Nov 17 at 7:54
Long
4418
I tried to pass to GridSearchCV other scoring metrics like balanced_accuracy for Binary Classification (instead of the default accuracy)
scoring = ['balanced_accuracy','recall','roc_auc','f1','precision']
validator = GridSearchCV(estimator=clf, param_grid=param_grid, scoring=scoring, refit=refit_scorer, cv=cv)
and got this error
ValueError: 'balanced_accuracy' is not a valid scoring value. Valid
options are
['accuracy','adjusted_mutual_info_score','adjusted_rand_score','average_precision','completeness_score','explained_variance','f1','f1_macro','f1_micro','f1_samples','f1_weighted','fowlkes_mallows_score','homogeneity_score','mutual_info_score','neg_log_loss','neg_mean_absolute_error','neg_mean_squared_error','neg_mean_squared_log_error','neg_median_absolute_error','normalized_mutual_info_score','precision','precision_macro','precision_micro','precision_samples','precision_weighted','r2','recall','recall_macro','recall_micro','recall_samples','recall_weighted','roc_auc','v_measure_score']
This is strange because 'balanced_accuracy' should be valid
Without defining balanced_accuracy then the code works fine
scoring = ['recall','roc_auc','f1','precision']
Also the scoring metrics in the error above seems to be different from the ones in the document
Any ideas why? Thank you so much
scikit-learn version is 0.19.2
python machine-learning scikit-learn metrics
python machine-learning scikit-learn metrics
asked Nov 17 at 7:54
Long
4418
asked Nov 17 at 7:54
Long
4418
edited Nov 17 at 14:53
asked Nov 17 at 7:54
Long
4418
asked Nov 17 at 7:54
Long
4418
asked Nov 17 at 7:54
Long
4418
4418
3
Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it withsklearn.__version__))
– desertnaut
Nov 17 at 8:58
Thank you I added the code andscikit-learnversion
– Long
Nov 17 at 14:54
add a comment |
3
Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it withsklearn.__version__))
– desertnaut
Nov 17 at 8:58
Thank you I added the code andscikit-learnversion
– Long
Nov 17 at 14:54
3
3
Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it with
sklearn.__version__))– desertnaut
Nov 17 at 8:58
Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it with
sklearn.__version__))– desertnaut
Nov 17 at 8:58
Thank you I added the code and
scikit-learn version– Long
Nov 17 at 14:54
Thank you I added the code and
scikit-learn version– Long
Nov 17 at 14:54
add a comment |
1 Answer
1
active
oldest
votes
up vote
2
down vote
accepted
Update your sklearn to the latest version if you want to use balanced_accuracy. As you can see from the 0.19 documentation balanced_accuracy is not a valid scoring metric. It was added in 0.20.
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
Update your sklearn to the latest version if you want to use balanced_accuracy. As you can see from the 0.19 documentation balanced_accuracy is not a valid scoring metric. It was added in 0.20.
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
add a comment |
up vote
2
down vote
accepted
Update your sklearn to the latest version if you want to use balanced_accuracy. As you can see from the 0.19 documentation balanced_accuracy is not a valid scoring metric. It was added in 0.20.
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
Update your sklearn to the latest version if you want to use balanced_accuracy. As you can see from the 0.19 documentation balanced_accuracy is not a valid scoring metric. It was added in 0.20.
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
Update your sklearn to the latest version if you want to use balanced_accuracy. As you can see from the 0.19 documentation balanced_accuracy is not a valid scoring metric. It was added in 0.20.
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
answered Nov 17 at 15:03
Mihai Chelaru
2,0177820
2,0177820
add a comment |
add a comment |
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Please post the relevant code - as it is, there are not enough details for the question to be answered meaningfully. Also post your scikit-learn version (you can get it with
sklearn.__version__))– desertnaut
Nov 17 at 8:58
Thank you I added the code and
scikit-learnversion– Long
Nov 17 at 14:54