classifiable

简明释义

英[ˈklæsɪfaɪəbl]美[ˈklæsɪfaɪəbl]

adj. 可分类的

英英释义

单词用法

同义词

反义词

例句

1.Needless to say, it's the central word in Derrida, which is nevertheless not classifiable or categorizable, and so for that reason we can't really say Derrida is specifically a literary theorist.

不用说，这个词对德里达来说至关重要，然而我们确实无法对话语进行分类，因此，我们恐怕不能称,德里达为文学理论家。

2.The appraising range, complex degree of geological environment, classifiable standard in appraising geologic hazards and its importance are expounded in this paper.

阐述评估范围、地质环境条件复杂程度、地质灾害危险性在评估工作中的划分标准及其重要性。

3.The information was not easily classifiable.

此信息不易分类。

4.At the same time, the disadvantage of the ANN scale too large and the speed of classifiable identification too slow and so on are overcome too.

同时克服了神经网络规模过于庞大及分类识别速度慢等缺点，取得了减少分类过程中模式匹配搜索量的良好效果。

5.Thirdly, it is necessary to change financing methods and use classifiable ways to manage the projects so that the realization in both obligations and risks can be stressed.

第三，改变筹资方式，分级负责，各负其责，增强资金使用的责任意识和风险意识；

6.In addition to these two types of folds, however, we must mention another one, which is not easily classifiable as either anticline or syncline and is referred to as a monocline.

然而，除了上述两种褶皱外，我们还必须提到另外一种，这种褶皱不易归类为背斜或向斜，而被称为单斜褶皱。

7.The butterfly separation technology is drawn in the separation of solid particles, and the processing method of sludge is given classifiable .

并引人碟式分离技术分离固体颗粒渣，分类给出了残渣处理方法。

8.The research team found that the species were easily classifiable by their physical traits.

研究小组发现这些物种很容易根据其物理特征被分类。

9.The data collected from the survey is classifiable into several distinct categories.

从调查中收集的数据可以被分类为几个不同的类别。

10.Documents in the archive are classifiable according to their subject matter.

档案中的文件可以根据其主题被分类。

11.The software can process various inputs that are classifiable into different formats.

该软件可以处理各种输入，这些输入可以被分类为不同的格式。

12.In machine learning, images are often classifiable based on their content.

在机器学习中，图像通常可以根据其内容被分类。

作文

In the realm of data science, one of the most crucial tasks is the organization and categorization of information. The ability to sort data into meaningful groups can significantly enhance our understanding and interpretation of that data. This brings us to the concept of something being classifiable, which refers to the capacity of certain data or items to be grouped based on shared characteristics or criteria. For instance, in a research study, data collected from various sources might be classifiable into distinct categories such as age, gender, or geographical location. By doing so, researchers can analyze trends and draw conclusions that are more precise and actionable.The importance of classifiable data extends beyond academic research. In business, companies often rely on customer data to tailor their marketing strategies. When customer preferences and behaviors are classifiable, businesses can create targeted campaigns that resonate with specific audiences. For example, a clothing retailer might categorize its customers into groups based on purchase history, allowing them to send personalized promotions that cater to each group's unique tastes. This level of customization not only improves customer satisfaction but also boosts sales and enhances brand loyalty.Moreover, the concept of classifiable data is vital in the field of artificial intelligence (AI) and machine learning. Algorithms are designed to identify patterns within datasets, and for these algorithms to function effectively, the data must be classifiable. For instance, in image recognition technology, images are often categorized into labels such as 'cat', 'dog', or 'car'. This classification allows the AI to learn from examples and improve its accuracy over time. Therefore, the ability to have classifiable data is fundamental for training robust AI models that can perform complex tasks.Additionally, the process of making data classifiable often involves cleaning and preprocessing the data. This step ensures that the data is free from errors and inconsistencies, which could otherwise lead to misleading results. For example, if survey responses are not standardized, it may be challenging to categorize them accurately. Thus, ensuring that data is classifiable requires careful attention to detail during the data collection and preparation phases.In conclusion, the ability to classify data into classifiable categories is essential across various fields, from research to business and technology. It enables better analysis, targeted strategies, and improved outcomes. As we continue to generate and collect vast amounts of data, the significance of having classifiable information will only grow. Embracing methods and tools that facilitate this classification will be key to unlocking the full potential of our data-driven world.

在数据科学领域，最关键的任务之一就是信息的组织和分类。将数据分成有意义的组的能力可以显著增强我们对数据的理解和解释。这就引出了某物被称为可分类的概念，这指的是某些数据或项目能够根据共享特征或标准进行分组的能力。例如，在一项研究中，从各种来源收集的数据可能会被可分类为不同的类别，如年龄、性别或地理位置。通过这样做，研究人员可以分析趋势并得出更精确和可行的结论。可分类数据的重要性超越了学术研究。在商业中，公司通常依赖客户数据来定制其营销策略。当客户偏好和行为是可分类的时，企业可以创建针对特定受众的定向活动。例如，一家服装零售商可能会根据购买历史将其客户分类，从而能够向每个组发送符合其独特品味的个性化促销。这种定制化程度不仅提高了客户满意度，还提升了销售额和品牌忠诚度。此外，可分类数据的概念在人工智能（AI）和机器学习领域至关重要。算法被设计用来识别数据集中的模式，而为了使这些算法有效运作，数据必须是可分类的。例如，在图像识别技术中，图像通常被归类为“猫”、“狗”或“汽车”等标签。这种分类使得AI能够从示例中学习并随着时间的推移提高其准确性。因此，拥有可分类数据的能力对于训练能够执行复杂任务的强大AI模型至关重要。此外，使数据变得可分类的过程通常涉及数据清理和预处理。这一步骤确保数据没有错误和不一致，否则可能导致误导性结果。例如，如果调查响应没有标准化，可能很难准确分类它们。因此，确保数据是可分类的需要在数据收集和准备阶段仔细关注细节。总之，将数据分类为可分类的类别的能力在各个领域都是必不可少的，从研究到商业再到技术。它使得更好的分析、目标策略和改进的结果成为可能。随着我们继续生成和收集大量数据，拥有可分类的信息的重要性只会增加。采用促进这种分类的方法和工具将是解锁我们数据驱动世界全部潜力的关键。