descriptor

简明释义

英[dɪˈskrɪptə(r)]美[dɪˈskrɪptər]

n. 描述符号

英英释义

单词用法

同义词

反义词

例句

1.Message descriptor (Message header).

消息描述符(消息头)。

2.Double click on the deployment descriptor.

双击部署描述符。

3.Fd is the file descriptor of the device file.

fd是设备文件的文件描述符。

4.Save the descriptor file.

保存描述符文件。

5.The descriptor should similar to Figure 15.

描述符应类似于图15。

6.Register the facelets-taglib descriptor file.

注册facelets -taglib描述符文件。

7.There is no need for any deployment descriptor in this case.

这种情况下不需要任何部署描述符。

8.There are two versions of the message descriptor.

消息描述符有两个版本。

9.In the database, each record has a unique descriptor that identifies it.

在数据库中，每条记录都有一个唯一的描述符来识别它。

10.The software uses a descriptor to define the properties of each object.

该软件使用一个描述符来定义每个对象的属性。

11.We need to add a descriptor for the new product in our inventory system.

我们需要在库存系统中为新产品添加一个描述符。

12.The descriptor for this image includes its dimensions and color profile.

这张图片的描述符包括其尺寸和颜色配置文件。

13.Each descriptor in the API documentation helps developers understand how to use the functions.

API文档中的每个描述符都帮助开发者理解如何使用这些函数。

作文

In the realm of data science and machine learning, understanding the concept of a descriptor is crucial for anyone looking to analyze or model data effectively. A descriptor can be defined as a characteristic or feature that helps to describe an object or a data point. These features can be numerical values, categorical labels, or even textual information that collectively provide insights into the underlying patterns within the data. For instance, in image processing, a descriptor might refer to specific attributes of an image, such as color, texture, and shape, which can be quantified and used for classification tasks. When working with datasets, identifying the right descriptors is vital for building accurate predictive models. In supervised learning, the choice of descriptors directly impacts the model's performance. If the selected descriptors do not capture the essence of the data, the model may struggle to learn and generalize from it. Therefore, data scientists often spend considerable time on feature selection and engineering to ensure that the most relevant descriptors are utilized. Another important aspect of descriptors is their ability to enhance the interpretability of a model. When a model's predictions can be traced back to specific descriptors, it becomes easier for stakeholders to understand how decisions are made. For example, in a healthcare setting, if a model predicts the risk of a disease based on certain descriptors like age, weight, and cholesterol levels, doctors can use this information to make informed decisions about patient care. Moreover, the concept of descriptor is not limited to quantitative data; it also extends to qualitative analyses. In natural language processing (NLP), words and phrases can serve as descriptors to represent sentiments or themes within a body of text. By analyzing these descriptors, algorithms can classify texts, summarize content, or even generate new text based on learned patterns. In conclusion, a descriptor plays a pivotal role in the field of data analysis and modeling. It serves as a bridge between raw data and meaningful insights, allowing researchers and practitioners to uncover trends and make predictions. By carefully selecting and utilizing appropriate descriptors, one can significantly improve the effectiveness of data-driven decision-making processes. As technology continues to evolve, the importance of understanding and leveraging descriptors will only grow, making it an essential skill for future data professionals.

在数据科学和机器学习领域，理解“descriptor”这一概念对任何希望有效分析或建模数据的人来说都是至关重要的。“descriptor”可以定义为一种特征或属性，用于描述对象或数据点。这些特征可以是数值、分类标签，甚至是文本信息，它们共同提供了对数据中潜在模式的洞察。例如，在图像处理过程中，“descriptor”可能指的是图像的特定属性，如颜色、纹理和形状，这些属性可以被量化并用于分类任务。 在处理数据集时，识别正确的“descriptor”对于构建准确的预测模型至关重要。在监督学习中，所选的“descriptor”直接影响模型的性能。如果所选的“descriptor”未能捕捉到数据的本质，模型可能会难以从中学习和泛化。因此，数据科学家通常花费大量时间进行特征选择和工程，以确保使用最相关的“descriptor”。 “descriptor”的另一个重要方面是它们增强模型可解释性的能力。当模型的预测可以追溯到特定的“descriptor”时，利益相关者更容易理解决策的形成过程。例如，在医疗保健环境中，如果模型根据某些“descriptor”如年龄、体重和胆固醇水平来预测疾病风险，医生可以利用这些信息做出有关患者护理的知情决策。 此外，“descriptor”的概念不仅限于定量数据；它还扩展到定性分析。在自然语言处理（NLP）中，单词和短语可以作为“descriptor”来表示文本中的情感或主题。通过分析这些“descriptor”，算法可以对文本进行分类、总结内容，甚至基于学习到的模式生成新文本。 总之，“descriptor”在数据分析和建模领域发挥着重要作用。它充当了原始数据与有意义洞察之间的桥梁，使研究人员和从业者能够发现趋势并做出预测。通过仔细选择和利用适当的“descriptor”，人们可以显著提高数据驱动决策过程的有效性。随着技术的不断发展，理解和利用“descriptor”的重要性将只会增加，使其成为未来数据专业人士必备的技能。