CODEWITHSUNDEEP
pythonscikit-learn
PV8
PV8

Reputation: 6260

Balanced_accuracy is not a valid scoring value in scikit-learn

super simliar to this post: ValueError: 'balanced_accuracy' is not a valid scoring value in scikit-learn

I am using:

scoring = ['precision_macro', 'recall_macro', 'balanced_accuracy_score']
clf = DecisionTreeClassifier(random_state=0)
scores = cross_validate(clf, X, y, scoring=scoring, cv=10, return_train_score=True)

And i receive the error:

ValueError: 'balanced_accuracy_score' is not a valid scoring value. Use sorted(sklearn.metrics.SCORERS.keys()) to get valid options.

I did the recommended solution and upgraded scikit (in the enviornment): enter image description here

When I check the possible scorers:

sklearn.metrics.SCORERS.keys()
dict_keys(['explained_variance', 'r2', 'max_error', 'neg_median_absolute_error', 'neg_mean_absolute_error', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_root_mean_squared_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance', 'accuracy', 'roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'roc_auc_ovr_weighted', 'roc_auc_ovo_weighted', 'balanced_accuracy', 'average_precision', 'neg_log_loss', 'neg_brier_score', 'adjusted_rand_score', 'homogeneity_score', 'completeness_score', 'v_measure_score', 'mutual_info_score', 'adjusted_mutual_info_score', 'normalized_mutual_info_score', 'fowlkes_mallows_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted'])

I can stil not find it? Where is the problem?

Upvotes: 5

Views: 5860

Answers (1)

Mihai Chelaru
Mihai Chelaru

Reputation: 8187

According to the docs for valid scorers, the value of the scoring parameter corresponding to the balanced_accuracy_score scorer function is "balanced_accuracy" as in my other answer:

Change:

scoring = ['precision_macro', 'recall_macro', 'balanced_accuracy_score']

to:

scoring = ['precision_macro', 'recall_macro', 'balanced_accuracy']

and it should work.

I do find the documentation a bit lacking in this respect, and this convention of removing the _score suffix is not consistent either, as all the clustering metrics still have _score in their names in their scoring parameter values.

Upvotes: 7

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