sklearn模块学习之-交叉验证cross_val_score

sklearn模块学习之-交叉验证cross_val_score

实例

from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC
from sklearn import datasets
iris = datasets.load_iris()

clf = SVC(kernel='linear', C=1)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)#训练集及标签，交叉次数5

print(scores)

scores1 = cross_val_score(clf, iris.data, iris.target, cv=10,scoring='precision').mean()#scoring='precision'评分标准，.mean()取平均
    scores2 = cross_val_score(clf, iris.data, iris.target,  cv=10,scoring='recall').mean()
    scores3 = cross_val_score(clf, iris.data, iris.target,  cv=10,scoring='f1').mean()
    scores4 = cross_val_score(clf, iris.data, iris.target,  cv=10,scoring='accuracy').mean()

#其他评分标准：
#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']