preprocess

简明释义

英[priːˈprəʊses]美[priːˈprɑːses]

v. 预处理，预加工

第 三 人 称 单 数 p r e p r o c e s s e s

现 在 分 词 p r e p r o c e s s i n g

过 去 式 p r e p r o c e s s e d

过 去 分 词 p r e p r o c e s s e d

英英释义

单词用法

同义词

反义词

例句

1.On the preprocess technology and the feasible conditions of pure cotton jean.

讨论纯棉牛仔布的前处理工艺过程及工艺条件。

2.Remember: On the Preprocess TAB, you can remove columns from the data set.

还记得么：在Preprocess选项卡，可以从数据集中删除列。

3.Instead of the brain processing all scent information, for instance, it seems nasal neurons preprocess some of it—almost as if the nose has its own small brain.

譬如在处理嗅觉信息时，并不是所有的工作都由大脑来承担。鼻神经元似乎也承担了部分工作——鼻腔好象自己有自己的小脑一样。

4.With TRaX, it's not too difficult to preprocess an XML document through a filter.

有了TRaX，通过过滤器，对XML文档进行预处理是不太困难的。

5.I originally thought I would preprocess the input XML documents: Then the links would be correct by the time they reach the XSLT processor.

我原来想要预处理输入文档：那么当链接到达XSLT处理器时，它们是正确的。

6.Based on the trajectory preprocess arithmetic, a kind of rate-variant square spiral scan strategy is designed according to the analysis results.

首先分析比较了水平行扫、螺旋扫描和分行螺旋扫描策略，并根据分析结果，结合轨迹预处理算法，设计了一种变速分行螺旋扫描策略。

7.The Informix ESQL/C product provides new libraries that are called when you use the esql command to preprocess your files to work with DB2.

InformixESQL/C产品提供了新的库，在通过使用 esql命令预处理您的文件来使用DB2时，可以调用这个库。

8.The HC compiler will preprocess Listing 2 and, using the @xpath comments, generate the state-tracking code automatically.

HC编译器将对清单2 进行预处理，并使用 @xpath注释自动生成状态跟踪代码。

9.In machine learning, it's essential to preprocess 预处理 the input features for better model performance.

在机器学习中，预处理输入特征对于提高模型性能至关重要。

10.The software can preprocess 预处理 images to enhance their quality before analysis.

该软件可以在分析之前对图像进行预处理以提高其质量。

11.Before feeding the data into the model, we need to preprocess 预处理 it to remove any noise.

在将数据输入模型之前，我们需要对其进行预处理以去除任何噪声。

12.The first step in data mining is to preprocess 预处理 the raw data into a usable format.

数据挖掘的第一步是将原始数据预处理为可用格式。

13.To improve accuracy, we must preprocess 预处理 the text data by removing stop words.

为了提高准确性，我们必须通过去除停用词来预处理文本数据。

作文

In the age of big data, the importance of data analysis cannot be overstated. However, before any meaningful analysis can take place, it is crucial to preprocess the data. To preprocess means to prepare or clean the data so that it is suitable for analysis. This step is vital because raw data often contains errors, inconsistencies, and irrelevant information that can skew results. By taking the time to preprocess the data, analysts can ensure that their findings are accurate and reliable.The first step in the preprocess stage typically involves data cleaning. This may include removing duplicate entries, correcting typos, and addressing missing values. For instance, if a dataset contains customer information with multiple entries for the same individual, these duplicates must be identified and eliminated. Similarly, if there are gaps in the data, such as missing ages or addresses, these should be filled in or removed, depending on the context of the analysis.Next, normalization and transformation are often necessary during the preprocess phase. Normalization involves adjusting the values in the dataset to a common scale without distorting differences in the ranges of values. This is especially important when dealing with variables measured in different units. For example, if one variable is measured in dollars and another in percentages, normalization allows for a fair comparison between them.Transformation might also involve converting categorical data into numerical formats, which is essential for certain types of analysis. For example, if a dataset includes a column for 'Gender' with values like 'Male' and 'Female', these could be transformed into numerical codes, such as 0 for Male and 1 for Female. This transformation allows algorithms to process the data more effectively.Another critical aspect of preprocessing data is feature selection. Not all variables in a dataset are relevant to the analysis at hand. Feature selection involves identifying and retaining only those variables that contribute significantly to the outcome being studied. This not only simplifies the analysis but also improves the model's performance by reducing noise from irrelevant data.Once the data has been cleaned, normalized, and relevant features selected, it is ready for analysis. Analysts can now apply various techniques, such as statistical analysis, machine learning, or data visualization, to derive insights from the data. The quality of these insights heavily relies on the effectiveness of the preprocessing stage. If the data is poorly preprocessed, the analysis may lead to incorrect conclusions, wasted resources, and misguided strategies.In conclusion, the preprocess stage is a fundamental part of data analysis that should not be overlooked. It sets the foundation for accurate and meaningful insights. By investing time and effort into properly preprocessing data, analysts can enhance the reliability of their findings and ultimately drive better decision-making within organizations. Therefore, understanding how to preprocess data effectively is a skill that every data analyst should master in order to succeed in today's data-driven world.

在大数据时代，数据分析的重要性不容小觑。然而，在进行任何有意义的分析之前，关键是要对数据进行预处理。预处理是指准备或清理数据，以使其适合分析。这一步至关重要，因为原始数据通常包含错误、不一致和无关信息，这可能会扭曲结果。通过花时间对数据进行预处理，分析师可以确保他们的发现是准确和可靠的。预处理阶段的第一步通常涉及数据清理。这可能包括删除重复条目、纠正错别字和处理缺失值。例如，如果一个数据集中包含多个同一客户的信息条目，则必须识别并消除这些重复项。同样，如果数据中存在空白，例如缺失的年龄或地址，则应根据分析的上下文进行填充或删除。接下来，在预处理阶段，规范化和转换通常是必要的。规范化涉及将数据集中值调整到一个公共尺度，而不扭曲值范围之间的差异。当处理以不同单位测量的变量时，这一点尤其重要。例如，如果一个变量以美元为单位测量，而另一个以百分比为单位测量，则规范化允许对它们进行公平比较。转换还可能涉及将分类数据转换为数值格式，这对于某些类型的分析至关重要。例如，如果数据集中包含一列“性别”，其值为“男性”和“女性”，则可以将其转换为数值代码，比如男性为0，女性为1。这种转换使算法能够更有效地处理数据。预处理数据的另一个关键方面是特征选择。数据集中的并非所有变量都与正在进行的分析相关。特征选择涉及识别并保留仅对所研究结果有显著贡献的变量。这不仅简化了分析，而且通过减少来自无关数据的噪音来提高模型的性能。一旦数据经过清理、规范化，并选择了相关特征，就可以进行分析。分析师现在可以应用各种技术，如统计分析、机器学习或数据可视化，从数据中得出洞察。这些洞察的质量在很大程度上依赖于预处理阶段的有效性。如果数据处理不当，分析可能导致错误的结论、资源浪费和误导性的策略。总之，预处理阶段是数据分析的一个基本部分，不应被忽视。它为准确和有意义的洞察奠定了基础。通过投入时间和精力来正确预处理数据，分析师可以提高其发现的可靠性，最终推动组织内更好的决策。因此，理解如何有效地预处理数据是每个数据分析师应该掌握的技能，以便在当今数据驱动的世界中取得成功。