cross val score scoring options

# sklearn cross_val_score scoring options

# For Regression
'explained_variance'
'max_error'
'neg_mean_absolute_error'
'neg_mean_squared_error'
'neg_root_mean_squared_error'
'neg_mean_squared_log_error'
'neg_median_absolute_error'
'r2'
'neg_mean_poisson_deviance'
'neg_mean_gamma_deviance'
'neg_mean_absolute_percentage_error'

# For Classification
'accuracy'
'balanced_accuracy'
'top_k_accuracy'
'average_precision'
'neg_brier_score'
'f1'
'f1_micro'
'f1_macro'
'f1_weighted'
'f1_samples'
'neg_log_loss'
'precision' 
'recall' 
'jaccard' 
'roc_auc'
'roc_auc_ovr'
'roc_auc_ovo'
'roc_auc_ovr_weighted'
'roc_auc_ovo_weighted'

# For Clustering
'adjusted_mutual info score'
'adjusted_rand_score'
'completeness_score'
'fowlkes_mallows_score'
'homogeneity_score'
'mutual_info_score'
'normalized_mutual_info_score'
'rand_score'
'v_measure_score'

Posted by: Guest on August-17-2021

>>> from sklearn import svm, cross_validation, datasets >>> iris = datasets.load_iris() >>> X, y = iris.data, iris.target >>> model = svm.SVC() >>> cross_validation.cross_val_score(model, X, y, scoring='wrong_choice') Traceback (most recent call last): ValueError: 'wrong_choice' is not a valid scoring value. Valid options are ['accuracy', 'adjusted_rand_score', 'average_precision', 'f1', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'precision', 'r2', 'recall', 'roc_auc']

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