identification probability

简明释义

识别概率

英英释义

例句

1.The forensic team calculated the identification probability 识别概率 of the DNA sample found at the crime scene to determine if it matched the suspect.

法医团队计算了犯罪现场发现的DNA样本的识别概率 识别概率，以确定它是否与嫌疑人匹配。

2.Researchers found that the identification probability 识别概率 increases significantly with better quality images.

研究人员发现，随着图像质量的提高，识别概率 识别概率显著增加。

3.In wildlife studies, researchers often assess the identification probability 识别概率 of different animal species through their unique markings.

在野生动物研究中，研究人员通常通过动物的独特标记来评估不同物种的识别概率 识别概率。

4.In a study of fingerprints, the identification probability 识别概率 was determined to be over 95% for high-resolution prints.

在指纹研究中，高分辨率指纹的识别概率 识别概率被确定为超过95%。

5.The software uses advanced algorithms to improve the identification probability 识别概率 of faces in crowded environments.

该软件使用先进的算法来提高拥挤环境中面部的识别概率 识别概率。

作文

In the realm of statistics and data analysis, the term identification probability refers to the likelihood that a particular individual or item can be correctly identified from a set of data. This concept plays a crucial role in various fields, including forensic science, marketing research, and machine learning. Understanding identification probability is essential for professionals who rely on data-driven decisions to draw accurate conclusions.For instance, in forensic science, the ability to identify a suspect based on DNA evidence hinges on the identification probability. When a DNA sample is collected from a crime scene, forensic analysts compare it to a database of known samples. The identification probability indicates how likely it is that the DNA belongs to a specific individual rather than another person with a similar genetic profile. A high identification probability strengthens the case against a suspect, while a low probability may lead to reasonable doubt.In marketing research, businesses often conduct surveys to understand consumer preferences. The identification probability comes into play when analyzing the effectiveness of targeted advertising campaigns. If a company can accurately identify the segment of the population that is most likely to respond to their advertisements, they can allocate resources more efficiently. A higher identification probability means that the company can confidently tailor its marketing strategies to the right audience, ultimately increasing sales and customer satisfaction.Machine learning algorithms also utilize the concept of identification probability to improve their predictive capabilities. For example, in classification tasks where an algorithm predicts whether an email is spam or not, the identification probability helps determine the confidence level of the prediction. If the algorithm assigns a high identification probability to an email being spam, users can trust the system's judgment. Conversely, a low probability might warrant further review by the user, ensuring that important emails are not mistakenly classified as spam.Moreover, understanding identification probability allows researchers to assess the reliability of their findings. In scientific studies, researchers often deal with a certain degree of uncertainty in their data. By calculating the identification probability, they can quantify this uncertainty and make informed decisions about the validity of their hypotheses. This statistical insight is invaluable for drawing robust conclusions and advancing knowledge in various scientific disciplines.In conclusion, identification probability is a pivotal concept that transcends multiple fields, from forensic science to marketing and machine learning. Its significance lies in the ability to make informed decisions based on data analysis. As we continue to generate and analyze vast amounts of data in today's digital age, a thorough understanding of identification probability will become increasingly important for professionals seeking to derive meaningful insights and drive effective strategies. By mastering this concept, individuals can enhance their analytical skills and contribute to more accurate and reliable outcomes in their respective fields.

在统计学和数据分析领域，术语identification probability（识别概率）指的是能够从一组数据中正确识别特定个体或项目的可能性。这个概念在法医学、市场研究和机器学习等多个领域都发挥着至关重要的作用。理解identification probability对于依赖数据驱动决策以得出准确结论的专业人士来说至关重要。例如，在法医学中，基于DNA证据识别嫌疑人的能力依赖于identification probability。当从犯罪现场收集DNA样本时，法医分析师会将其与已知样本数据库进行比较。identification probability表示该DNA样本属于特定个体而不是另一位具有相似基因特征的人的可能性。高identification probability增强了对嫌疑人的案件，而低概率可能导致合理怀疑。在市场研究中，企业通常会进行调查以了解消费者偏好。当分析广告活动的有效性时，identification probability就发挥了作用。如果公司能够准确识别出最有可能响应其广告的人群，就可以更有效地分配资源。较高的identification probability意味着公司可以自信地将其营销策略调整到正确的受众，从而最终提高销售额和客户满意度。机器学习算法同样利用identification probability的概念来提高预测能力。例如，在分类任务中，算法预测一封电子邮件是否为垃圾邮件，identification probability帮助确定预测的置信水平。如果算法将一封电子邮件标记为垃圾邮件的identification probability很高，用户可以相信系统的判断。相反，如果概率较低，则可能需要用户进一步审查，以确保重要邮件不会被错误分类为垃圾邮件。此外，理解identification probability使研究人员能够评估其发现的可靠性。在科学研究中，研究人员经常面临数据的不确定性。通过计算identification probability，他们可以量化这种不确定性，并就假设的有效性做出明智的决策。这种统计洞察力对于得出稳健的结论并推动各个科学学科的知识进步是不可或缺的。总之，identification probability是一个关键概念，跨越多个领域，从法医学到市场营销和机器学习。其重要性在于能够基于数据分析做出明智的决策。随着我们在数字时代继续生成和分析大量数据，深入理解identification probability将变得越来越重要，尤其是对于那些寻求从中获取有意义见解并推动有效策略的专业人士。通过掌握这一概念，个人可以提升其分析技能，并为各自领域的更准确和可靠的结果做出贡献。