preprocessing

简明释义

英[/ˌpriːˈprɒsɛsɪŋ/]美[/ˌpriːˈprɒsɛsɪŋ/]

n. 预处理；预加工

英英释义

单词用法

同义词

反义词

例句

1.Iris image preprocessing includes four parts: iris localization, iris normalization, iris image enhancement and choice of the valid region.

虹膜图像的预处理主要分为四步：内外边缘定位、图像归一化、图像增强和有效区域的划分。

2.A preprocessing step performs macro substitution on program text, inclusion of other source files, and conditional compilation.

编译的预处理阶段对程序文本进行宏替换，包含进其他源文件，并进行条件编译。

3.This kind of preprocessing is a good idea — you can never really trust user input 100 percent.

进行这类预处理不失为一个好的做法 —不能百分之百相信用户的输入。

4.A preprocessing circuit is designed for analog signal transformation because of large inner impedance of biosensor and bipolar signal.

针对采用的生物传感器内阻高、信号弱、双极性等特点，用预处理电路对模拟信号进行了变换。

5.SAX does very little preprocessing, so the programmer has to manage many low-level aspects.

SAX几乎不做预处理，因而，程序员不得不管理很多低级别问题。

6.These files might require preprocessing to put them into an appropriate format.

这些文件可能需要进行预处理，以便将它们转换为适当的格式。

7.The following work has been performed in this research: The ECG preprocessing and characteristic point detection are studied.

本论文主要内容包括心电信号预处理技术、特征点检测技术以及诊断技术的研究。

8.I fit into it, you fit into it, and a hundred years tops, my body can return to the Earth with no preprocessing.

我能溶解于其中，你也可以，一百年以后，我的身体可以不用处理就回归到地球。

9.Image preprocessing 预处理 is essential for improving the accuracy of computer vision algorithms.

图像预处理 preprocessing对于提高计算机视觉算法的准确性至关重要。

10.The effectiveness of a machine learning model heavily relies on the quality of preprocessing 预处理 applied to the training data.

机器学习模型的有效性在很大程度上依赖于对训练数据应用的预处理 preprocessing质量。

11.Before training the model, we need to perform preprocessing 预处理 on the dataset to clean the data.

在训练模型之前，我们需要对数据集进行预处理 preprocessing以清理数据。

12.In natural language processing, text preprocessing 预处理 often involves tokenization and removing stop words.

在自然语言处理过程中，文本预处理 preprocessing通常涉及分词和去除停用词。

13.The preprocessing 预处理 steps include normalization and scaling of the input features.

该预处理 preprocessing步骤包括输入特征的归一化和缩放。

作文

In today's data-driven world, the importance of effective data management cannot be overstated. One crucial step in this process is preprocessing, which refers to the techniques applied to raw data before it is used for analysis or modeling. Preprocessing plays a vital role in ensuring the quality and reliability of the results obtained from data analysis. Without proper preprocessing, the conclusions drawn from data can be misleading or entirely incorrect.The first aspect of preprocessing involves cleaning the data. Raw data often contains errors, inconsistencies, or irrelevant information that can skew results. For instance, in a dataset containing customer information, there might be duplicate entries or missing values. If these issues are not addressed through preprocessing, they could lead to inaccurate insights about customer behavior. Therefore, data cleaning is an essential step that helps in removing or correcting such discrepancies.Another important part of preprocessing is normalization. This step is particularly significant when dealing with numerical data that may vary widely in scale. For example, if one feature represents income in thousands while another represents age in years, the disparity in their scales can lead to biased outcomes during analysis. Normalization ensures that each feature contributes equally to the analysis by adjusting their scales, thus enhancing the model's performance.Furthermore, preprocessing also includes transforming categorical data into numerical formats. Many machine learning algorithms require numerical input; therefore, converting categories into numbers is necessary. Techniques such as one-hot encoding or label encoding are commonly used in this stage. By applying these methods during preprocessing, we can effectively prepare categorical variables for modeling.Feature selection is another critical component of preprocessing. Not all features in a dataset are relevant for the analysis at hand. Including irrelevant features can introduce noise and reduce the model's accuracy. Through techniques like correlation analysis or recursive feature elimination, we can identify and retain only the most significant features, thereby improving the efficiency and effectiveness of our models.Moreover, the role of preprocessing extends to the creation of new features. Sometimes, existing data can be transformed or combined to create new, more informative features. This process, known as feature engineering, can significantly impact the performance of machine learning models. By deriving new insights from existing data, we can enhance the predictive power of our models.In summary, preprocessing is an indispensable step in data analysis that ensures the quality and reliability of results. It encompasses various techniques such as data cleaning, normalization, transformation of categorical data, feature selection, and feature engineering. Each of these components contributes to preparing the data for effective analysis and modeling. As we continue to navigate an increasingly data-centric world, mastering the art of preprocessing will undoubtedly enhance our ability to derive meaningful insights from complex datasets. Ultimately, investing time and effort into preprocessing can lead to more accurate and actionable results, which are crucial for informed decision-making in any field.

在当今数据驱动的世界中，有效的数据管理的重要性不容小觑。这个过程中的一个关键步骤是预处理，它指的是在数据分析或建模之前对原始数据应用的技术。预处理在确保数据分析结果的质量和可靠性方面发挥着至关重要的作用。如果没有适当的预处理，从数据中得出的结论可能会误导甚至完全错误。预处理的第一部分涉及清理数据。原始数据通常包含错误、不一致或无关的信息，这可能会扭曲结果。例如，在包含客户信息的数据集中，可能存在重复条目或缺失值。如果这些问题没有通过预处理来解决，它们可能会导致关于客户行为的不准确见解。因此，数据清理是一个必不可少的步骤，有助于消除或纠正这些差异。预处理的另一个重要部分是归一化。当处理数值数据时，归一化尤其重要，因为这些数据的规模可能差异很大。例如，如果一个特征表示以千为单位的收入，而另一个特征表示以年为单位的年龄，那么它们规模之间的差异可能会导致分析结果偏差。归一化确保每个特征对分析的贡献相等，从而提高模型的性能。此外，预处理还包括将分类数据转换为数值格式。许多机器学习算法需要数值输入；因此，将类别转换为数字是必要的。在这一阶段，通常使用独热编码或标签编码等技术。通过在预处理过程中应用这些方法，我们可以有效地为建模准备分类变量。特征选择是预处理的另一个关键组成部分。数据集中并非所有特征都与当前分析相关。包括无关特征可能会引入噪声并降低模型的准确性。通过相关性分析或递归特征消除等技术，我们可以识别并保留最重要的特征，从而提高模型的效率和有效性。此外，预处理的作用还扩展到新特征的创建。有时，现有数据可以被转换或组合以创建新的、更具信息量的特征。这个过程被称为特征工程，可以显著影响机器学习模型的性能。通过从现有数据中提取新见解，我们可以增强模型的预测能力。总之，预处理是数据分析中不可或缺的一步，它确保了结果的质量和可靠性。它包括各种技术，如数据清理、归一化、分类数据转换、特征选择和特征工程。每个组成部分都有助于为有效分析和建模准备数据。随着我们继续在日益数据中心化的世界中前行，掌握预处理的艺术无疑将增强我们从复杂数据集中提取有意义见解的能力。最终，在预处理上投入时间和精力可以导致更准确和可操作的结果，这对任何领域的知情决策至关重要。