英语

一：false_positive是什么意思（中英文）解释的意思：

false_positive是指在二元分类（binary classification）中，模型预测为正类（positive class）但实际上是负类（negative class）的情况。即模型错误地将负类样本预测为正类，造成误判的情况。这种错误预测也被称为假阳性（false positive），与之相对应的是假阴性（false negative）。

False positive is a term used in binary classification, referring to the situation where a model predicts a positive class but it is actually a negative class. In other words, the model mistakenly predicts a negative sample as positive, resulting in a false judgment. This type of incorrect prediction is also known as false positive, and it is opposite to false negative.

二：怎么读（音标）：

false_positive [fɔːls pɑːzɪtɪv]

三：用法：

false_positive通常用于评估机器学习模型的性能，特别是在医学诊断、安全检测等领域。它可以帮助我们了解模型在不同阈值下的表现，并且可以通过调整阈值来降低误判率。

False positive is commonly used to evaluate the performance of machine learning models, especially in fields such as medical diagnosis and security detection. It can help us understand the performance of the model at different thresholds and can be reduced by adjusting the threshold.

四：例句1-5句且中英对照：

1. The new algorithm significantly reduces the false positive rate in detecting cancer cells.（新算法大大降低了癌细胞检测中的假阳性率。）

2. The security system has been updated to minimize false positives and improve accuracy.（安全已经更新，以最小化假阳性并提高准确性。）

3. It is important for doctors to consider both false positives and false negatives when interpreting medical test results.（医生在解读医学检测结果时，需要同时考虑假阳性和假阴性。）

4. The model has a high precision but a relatively high false positive rate, which may lead to unnecessary interventions.（该模型具有较高的精确度，但假阳性率相对较高，可能导致不必要的干预。）

5. The team is working on optimizing the model to reduce the number of false positives in real-time detection.（团队正在努力优化模型，以减少实时检测中的假阳性数量。）

五：同义词及用法：

与false_positive相关的同义词包括错误阳性（incorrect positive）、误报（false alarm）等。它们都指的是模型错误地将负类样本预测为正类的情况。

Related synonyms of false_positive include incorrect positive, false alarm, etc. They all refer to the situation where a model mistakenly predicts a negative sample as positive.

六：编辑总结：

false_positive是指二元分类中模型错误地将负类样本预测为正类的情况，也被称为假阳性。它通常用于评估机器学习模型的性能，并且可以通过调整阈值来降低误判率。同义词包括错误阳性、误报等。在使用过程中，我们需要注意平衡假阳性和假阴性，以达到最佳的预测效果。