多标签文本分类 指标计算公式

多标签文本分类指标计算公式
英文版
Multi-Label Text Classification: Metric Calculation Formulas Multi-label text classification is a crucial task in natural language processing (NLP) that involves assigning multiple labels to a given text. Accurate evaluation of such a task requires the use of appropriate evaluation metrics. In this article, we will explore the common evaluation metrics for multi-label text classification and provide the corresponding calculation formulas.
Accuracy: Accuracy is the simplest evaluation metric, defined as the ratio of correctly classified instances to the total number of instances. However, it can be misleading in the case of multi-label classification as it does not consider the不平衡性between classes.
(Accuracy = \frac{TP + TN}{TP + FP + FN + TN})
TP: True Positives (Correctly predicted positive instances)
TN: True Negatives (Correctly predicted negative instances) FP: False Positives (Incorrectly predicted positive instances) FN: False Negatives (Incorrectly predicted negative instances) Precision and Recall: Precision and recall are two widely used evaluation metrics in multi-label classification. Precision measures the proportion of positive instances that are correctly predicted, while recall measures the proportion of actual positive instances that are correctly predicted.
(Precision = \frac{TP}{TP + FP})
(Recall = \frac{TP}{TP + FN})
3. F1 Score: The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both precision and recall.
(F1 Score = 2 \times \frac{Precision \times Recall}{Precision + Recall})
4. Hamming Loss: Hamming Loss is a metric that measures the fraction of labels that are incorrectly predicted. It is
particularly useful in multi-label classification where the goal is to minimize the number of misclassified labels.
(Hamming Loss = \frac{1}{n} \sum_{i=1}^{n} \frac{1}{|L_i|}
\sum_{j \in L_i} xor(y_{ij}, \hat{y}_{ij}))
(n): Number of instances
(L_i): Set of true labels for instance (i)
(y_{ij}): True label for instance (i) and label (j)
(\hat{y}_{ij}): Predicted label for instance (i) and label (j)
(xor): Exclusive OR operation
Subset Accuracy: Subset Accuracy measures the fraction of instances whose entire set of true labels is correctly predicted. It is a strict metric that requires perfect prediction of all labels.
(Subset Accuracy = \frac{1}{n} \sum_{i=1}^{n} [L_i = \hat{L}_i]) (L_i): Set of true labels for instance (i)
(\hat{L}_i): Set of predicted labels for instance (i)
([L_i = \hat{L}_i]): Indicator function that returns 1 if (L_i) is equal to (\hat{L}_i), 0 otherwise.
In conclusion, accurate evaluation of multi-label text classification requires the use of appropriate evaluation metrics. The metrics discussed in this article, including accuracy, precision, recall, F1 score, Hamming loss, and subset accuracy, provide a comprehensive toolbox for assessing the performance of multi-label text classification models.
中文版
多标签文本分类：指标计算公式
多标签文本分类是自然语言处理（NLP）中的一项重要任务，它涉及为给定的文本分配多个标签。

为了准确评估这种任务，需要使用适当的评估指标。

在本文中，我们将探讨多标签文本分类的常见评估指标，并提供相应的计算公式。

准确率：准确率是最简单的评估指标，定义为正确分类的实例数与总实例数之比。

然而，在多标签分类的情况下，它可能会产生误导，因为它不考虑类别之间的不平衡性。

(准确率 = \frac{真正例 + 真反例}{真正例 + 假正例 + 假反例 + 真反例})
真正例（TP）：正确预测的正例实例
真反例（TN）：正确预测的反例实例
假正例（FP）：错误预测的正例实例
假反例（FN）：错误预测的反例实例
精确率和召回率：精确率和召回率是多标签分类中广泛使用的两个评估指标。

精确率衡量正确预测的正例实例的比例，而召回率衡量实际正例实例中正确预测的比例。

(精确率 = \frac{真正例}{真正例 + 假正例})
(召回率 = \frac{真正例}{真正例 + 假。