sklearn 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'