W3School TIY Editor

import numpy from sklearn import metrics # 生成模拟数据（实际标签和预测标签）： actual = numpy.random.binomial(1, 0.9, size=1000) # 实际标签（90% 概率为 1） predicted = numpy.random.binomial(1, 0.9, size=1000) # 预测标签（90% 概率为 1） # 计算各类评估指标： Accuracy = metrics.accuracy_score(actual, predicted) # 准确率 Precision = metrics.precision_score(actual, predicted) # 精确率 Sensitivity_recall = metrics.recall_score(actual, predicted) # 召回率/敏感度 Specificity = metrics.recall_score(actual, predicted, pos_label=0) # 特异度 F1_score = metrics.f1_score(actual, predicted) # F1 分数 # 输出评估指标结果： print({ "准确率 (Accuracy)": Accuracy, "精确率 (Precision)": Precision, "召回率/敏感度 (Sensitivity_recall)": Sensitivity_recall, "特异度 (Specificity)": Specificity, "F1 分数 (F1_score)": F1_score })