misclassified

简明释义

英[ˌmɪsˈklæs.ɪ.faɪd]美[ˌmɪsˈklæsəˌfaɪd]

vt. 对……进行错误的分类（misclassify 的过去式和过去分词）

英英释义

单词用法

同义词

反义词

例句

1.Powerwatch says the researchers misclassified 88 percent of the Danish population who began using mobile phones after 1995 for which there is no subscription information due to legal reasons.

Powerwatch机构揭示，研究人员对88%的丹麦人做了错误归类，因为有些人是1995年后才使用手机的，但不知何故，系统中并没有他们的登记信息。

2.The result was that almost a third of her volunteers looked, from the modern point of view, misclassified.

结果是，在今天的标准来看，几乎三分之一的志愿者被误诊了。

3.Powerwatch says the researchers misclassified 88 percent of the Danish population who began using mobile phones after 1995 for which there is no subscription information due to legal reasons.

Powerwatch机构揭示，研究人员对88%的丹麦人做了错误归类，因为有些人是1995年后才使用手机的，但不知何故，系统中并没有他们的登记信息。

4.The data points were misclassified, leading to inaccurate predictions.

数据点被错误分类，导致预测不准确。

5.The machine learning model struggled because many samples were misclassified during training.

由于许多样本在训练过程中被错误分类，机器学习模型遇到了困难。

6.The research paper highlighted the issues caused by misclassified data in clinical trials.

研究论文强调了临床试验中由错误分类数据引起的问题。

7.She realized that her expense report had several misclassified items.

她意识到她的费用报告中有几项被错误分类。

8.In the audit, several transactions were found to be misclassified in the financial records.

在审计中，发现财务记录中的几笔交易被错误分类。

作文

In the world of data analysis, one of the most common challenges is dealing with information that has been misclassified. The term misclassified refers to instances where data points are incorrectly labeled or categorized. This can occur in various fields, including machine learning, statistics, and even everyday decision-making processes. Understanding the implications of misclassified data is crucial for anyone involved in data-driven projects.For instance, consider a scenario in which a healthcare system uses an algorithm to predict patient outcomes based on historical data. If the data has been misclassified, the algorithm may produce inaccurate predictions, leading to poor treatment decisions. Patients who should receive immediate care might be overlooked, while others may undergo unnecessary procedures. This highlights the importance of accurate data classification in healthcare, where lives can be significantly impacted by small errors.Moreover, in the realm of machine learning, misclassified data can severely affect the performance of a model. When training a machine learning model, it learns from labeled data. If the training data contains many misclassified examples, the model will struggle to learn the correct patterns. As a result, its predictions will likely be inaccurate, which can lead to failures in applications ranging from image recognition to natural language processing.The ramifications of misclassified data extend beyond individual projects. In broader contexts, such as social media platforms, misclassified content can lead to misinformation spreading rapidly. For example, if a news article is incorrectly categorized as 'reliable' when it is actually 'fake news', users may unknowingly share harmful information. This can have significant consequences for public opinion and societal trust.To mitigate the risks associated with misclassified data, it is essential to implement robust validation processes. Regular audits of datasets can help identify and correct misclassified entries before they cause harm. Additionally, employing advanced algorithms that can detect anomalies or inconsistencies in data can also minimize the likelihood of misclassification.Furthermore, educating individuals about the potential for misclassified data is vital. Awareness can empower data users to critically evaluate the information they encounter. For instance, researchers and analysts should be trained to question the sources and classifications of the data they use. By fostering a culture of scrutiny and verification, the impact of misclassified data can be reduced.In conclusion, the issue of misclassified data is a significant concern across various domains. Its presence can lead to detrimental outcomes, from healthcare missteps to the spread of misinformation. Therefore, understanding what it means to be misclassified and taking proactive steps to address it is essential for anyone working with data. By prioritizing accuracy and validation, we can enhance the reliability of our analyses and foster a more informed society.

在数据分析的世界中，最常见的挑战之一是处理被错误分类的信息。术语错误分类指的是数据点被错误标记或分类的情况。这种情况可能发生在多个领域，包括机器学习、统计学，甚至是日常决策过程中。理解错误分类数据的影响对于任何参与数据驱动项目的人来说都是至关重要的。例如，考虑一个场景，其中医疗系统使用算法根据历史数据预测患者结果。如果数据被错误分类，算法可能会产生不准确的预测，从而导致治疗决策不当。应该立即接受治疗的患者可能会被忽视，而其他患者可能会接受不必要的程序。这突显了在医疗保健中准确数据分类的重要性，因为小错误可能会对生命产生重大影响。此外，在机器学习领域，错误分类的数据可能严重影响模型的性能。在训练机器学习模型时，它从标记的数据中学习。如果训练数据中包含许多错误分类的示例，模型将难以学习正确的模式。因此，它的预测可能不准确，这可能导致从图像识别到自然语言处理等应用的失败。错误分类数据的影响超出了个别项目。在更广泛的背景下，例如社交媒体平台，错误分类内容可能导致虚假信息迅速传播。例如，如果一篇新闻文章被错误地归类为“可靠”，而实际上它是“假新闻”，用户可能会在不知情的情况下分享有害信息。这可能对公众舆论和社会信任产生重大影响。为了减轻与错误分类数据相关的风险，实施强有力的验证过程是至关重要的。定期审核数据集可以帮助识别和纠正错误分类的条目，以防其造成伤害。此外，采用先进的算法来检测数据中的异常或不一致性也可以最大限度地减少错误分类的可能性。此外，教育个人关于错误分类数据的潜在性是至关重要的。意识可以使数据用户能够批判性地评估他们遇到的信息。例如，研究人员和分析师应接受培训，质疑他们使用的数据的来源和分类。通过培养审查和验证的文化，可以减少错误分类数据的影响。总之，错误分类数据的问题在各个领域都是一个重要的关注点。它的存在可能导致有害的结果，从医疗失误到虚假信息的传播。因此，理解什么是错误分类并采取主动措施来解决这一问题，对于任何与数据打交道的人来说都是至关重要的。通过优先考虑准确性和验证，我们可以增强分析的可靠性，促进一个更加信息化的社会。