我正在对数据集进行分类,并使用交叉验证进行建模。交叉验证给出了每次折叠的准确性,因为类别不平衡,准确性不是正确的衡量标准。我想要获得 AUC-ROC 而不是准确性。
最佳答案
cross_val_score
支持大量评分选项。
提到了详尽的列表 here .
['accuracy', 'recall_samples', 'f1_macro', 'adjusted_rand_score', 'recall_weighted', 'precision_weighted', 'recall_macro', 'homogeneity_score', 'neg_mean_squared_log_error', 'recall_micro', 'f1', 'neg_log_loss', 'roc_auc', 'average_precision', 'f1_weighted', 'r2', 'precision_macro', 'explained_variance', 'v_measure_score', 'neg_mean_absolute_error', 'completeness_score', 'fowlkes_mallows_score', 'f1_micro', 'precision_samples', 'mutual_info_score', 'neg_mean_squared_error', 'balanced_accuracy', 'neg_median_absolute_error', 'precision_micro', 'normalized_mutual_info_score', 'adjusted_mutual_info_score', 'precision', 'f1_samples', 'brier_score_loss', 'recall']
这是一个展示如何使用 auc_roc
的示例。
>>> from sklearn import datasets, linear_model
>>> from sklearn.model_selection import cross_val_score
>>> import numpy as np
>>> X, y = datasets.load_breast_cancer(return_X_y=True)
>>> model = linear_model.SGDClassifier(max_iter=50, random_state=7)
>>> print(cross_val_score(model, X, y, cv=5, scoring = 'roc_auc'))
[0.96382429 0.96996124 0.95573441 0.96646546 0.91113347]
关于python-3.x - 交叉验证时如何获取AUC-ROC而不是准确率?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/55769836/