sample function

简明释义

样本函数

英英释义

例句

1.To help you get started, we provide a sample function 示例函数 that demonstrates how to connect to the database.

为了帮助您入门，我们提供一个sample function 示例函数，演示如何连接到数据库。

2.The documentation includes a sample function 示例函数 for users to test the API easily.

文档中包含一个sample function 示例函数，供用户轻松测试API。

3.You can modify the sample function 示例函数 to suit your specific requirements.

您可以修改这个sample function 示例函数以满足您的特定需求。

4.In our software demo, we will showcase a sample function 示例函数 that calculates the total price of items in a shopping cart.

在我们的软件演示中，我们将展示一个sample function 示例函数，用于计算购物车中商品的总价。

5.The tutorial features a sample function 示例函数 to illustrate the concept of recursion.

教程中有一个sample function 示例函数，用来说明递归的概念。

作文

In the world of programming and data analysis, understanding the concept of a sample function is crucial for both beginners and advanced practitioners. A sample function can be defined as a specific type of function that takes a subset of data from a larger dataset to represent the whole. This concept is particularly important in statistics and machine learning, where working with large datasets can be computationally expensive and time-consuming. By using a sample function, one can derive insights without having to process every single piece of data.For instance, consider a scenario where a researcher wants to understand the average height of adult males in a country. It would be impractical to measure the height of every adult male. Instead, the researcher could use a sample function to select a random sample of individuals from the population. By measuring the heights of this smaller group, the researcher can estimate the average height for the entire population with a certain degree of confidence.The importance of a sample function extends beyond just efficiency; it also plays a critical role in ensuring the validity of statistical analyses. When selecting a sample, it is essential that the method used does not introduce bias. For example, if the researcher only measured heights in urban areas, the results may not accurately reflect the average height of the entire population, which includes rural areas as well. Therefore, a well-designed sample function should incorporate randomness and representativeness to ensure that the findings are generalizable.Moreover, in the field of machine learning, sample functions are often employed during the training phase of models. When building algorithms, data scientists frequently work with large datasets that can be cumbersome to handle in their entirety. By applying a sample function, they can create a smaller, more manageable dataset that still captures the essential characteristics of the larger set. This allows for quicker iterations and testing of models, ultimately speeding up the development process.Additionally, the use of a sample function can help in avoiding overfitting, which occurs when a model learns the noise in the training data rather than the underlying pattern. By utilizing a representative sample, data scientists can train their models on diverse examples, enhancing the model's ability to generalize to new, unseen data.In conclusion, the concept of a sample function is integral to effective data analysis and modeling. It allows researchers and data scientists to work efficiently while still maintaining the integrity of their findings. Whether in statistics or machine learning, the ability to draw meaningful conclusions from a carefully selected sample is a skill that can significantly impact the success of any data-driven project. As we continue to generate and collect vast amounts of data, mastering the use of a sample function will become increasingly important to ensure that we can derive valuable insights without being overwhelmed by the volume of information available to us.

在编程和数据分析的世界中，理解“sample function”这一概念对初学者和高级从业者来说都至关重要。“sample function”可以定义为一种特定类型的函数，它从更大的数据集中提取一个子集，以代表整体。这个概念在统计学和机器学习中尤为重要，因为处理大型数据集可能会消耗大量计算资源和时间。通过使用“sample function”，人们可以在不处理每一条数据的情况下获得洞察。例如，考虑一个研究者想要了解一个国家成年男性的平均身高的情景。测量每个成年男性的身高是不切实际的。相反，研究者可以使用“sample function”随机选择一小部分个体进行测量。通过测量这一小组的身高，研究者可以以一定的置信度估算整个群体的平均身高。“sample function”的重要性不仅在于效率；它在确保统计分析的有效性方面也发挥着关键作用。在选择样本时，所使用的方法必须不引入偏见。例如，如果研究者仅测量城市地区的身高，结果可能无法准确反映整个群体的平均身高，因为该群体还包括农村地区。因此，一个设计良好的“sample function”应包含随机性和代表性，以确保研究结果具有普遍适用性。此外，在机器学习领域，“sample function”通常在模型的训练阶段使用。当构建算法时，数据科学家常常处理庞大的数据集，这些数据集在整体上可能难以处理。通过应用“sample function”，他们可以创建一个更小、更易管理的数据集，同时仍然捕捉到较大数据集的基本特征。这使得模型的迭代和测试速度更快，从而加速开发过程。此外，使用“sample function”还可以帮助避免过拟合，这种情况发生在模型学习了训练数据中的噪声而不是潜在模式时。通过利用具有代表性的样本，数据科学家可以在多样化的例子上训练他们的模型，从而增强模型对新未见数据的泛化能力。总之，“sample function”的概念对于有效的数据分析和建模至关重要。它使研究人员和数据科学家能够高效工作，同时保持研究结果的完整性。无论是在统计学还是机器学习中，从精心选择的样本中得出有意义的结论的能力都是一种可以显著影响任何数据驱动项目成功的技能。随着我们继续生成和收集大量数据，掌握“sample function”的使用将变得越来越重要，以确保我们能够在不被信息量淹没的情况下获得有价值的洞察。