Valores predefinidos

Convención: Los valores más altos indican mejores modelos.

Estas métricas están disponibles en:

https://scikit-learn.org/stable/modules/model_evaluation.html

Listado de funciones disponibles

from sklearn.metrics import get_scorer_names

get_scorer_names()

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

Ejemplo

from sklearn import datasets, svm
from sklearn.model_selection import cross_val_score

X, y = datasets.load_iris(return_X_y=True)
clf = svm.SVC(random_state=0)
cross_val_score(clf, X, y, cv=5, scoring="accuracy")

array([0.96666667, 0.96666667, 0.96666667, 0.93333333, 1.        ])