artificial sample

简明释义

人工采样;

英英释义

例句

1.In our experiment, we used an artificial sample to test the new algorithm.

在我们的实验中，我们使用了一个人工样本来测试新的算法。

2.Using an artificial sample allowed us to control variables more effectively.

使用人工样本使我们能够更有效地控制变量。

3.An artificial sample can be useful in validating the accuracy of a predictive model.

一个人工样本在验证预测模型的准确性方面非常有用。

4.For the training session, we generated an artificial sample to help the model learn.

为了培训会议，我们生成了一个人工样本来帮助模型学习。

5.The researchers created an artificial sample to simulate real-world conditions.

研究人员创建了一个人工样本以模拟现实世界的条件。

作文

In the field of data science and machine learning, the term artificial sample refers to a data point or a set of data points that are generated through artificial means, rather than being collected from real-world observations. These samples can be created using algorithms, simulations, or other computational methods. The use of artificial sample data is crucial in various applications, especially when real data is scarce, expensive to obtain, or poses ethical concerns. For instance, in medical research, obtaining patient data can be challenging due to privacy laws and ethical considerations. Therefore, researchers may rely on artificial sample data to develop and test their models before applying them to actual patient data.One of the significant advantages of using artificial sample data is the ability to control for specific variables. By generating data with known characteristics, researchers can create scenarios that would be difficult or impossible to replicate in the real world. This controlled environment allows for more rigorous testing of hypotheses and can lead to more reliable outcomes. Additionally, artificial sample data can be used to augment existing datasets, providing more comprehensive training data for machine learning models. This is particularly useful in situations where the available data is imbalanced or lacks diversity.However, there are also challenges associated with the use of artificial sample data. One major concern is the risk of overfitting. When a model is trained on artificial sample data, it may learn patterns that do not exist in real-world data, leading to poor performance when applied outside the controlled environment. To mitigate this risk, it is essential to validate models using real-world data whenever possible. Furthermore, the quality of the artificial sample data is paramount; if the generated data does not accurately reflect the complexities of the real world, the insights drawn from it may be misleading.In conclusion, while artificial sample data plays a vital role in advancing research and technology, it is essential to approach its use with caution. Researchers must ensure that they understand the limitations and potential pitfalls of relying on artificially generated data. By combining artificial sample data with real-world observations, we can develop more robust models and gain deeper insights into the phenomena we seek to understand. As technology continues to evolve, the methods for generating and utilizing artificial sample data will likely become more sophisticated, further enhancing our ability to tackle complex problems across various domains.

在数据科学和机器学习领域，术语人工样本指的是通过人工手段生成的数据点或一组数据点，而不是从真实世界的观察中收集的。这些样本可以通过算法、模拟或其他计算方法创建。使用人工样本数据在各种应用中至关重要，尤其是在真实数据稀缺、获取成本高昂或存在伦理问题时。例如，在医学研究中，由于隐私法和伦理考虑，获取患者数据可能会面临挑战。因此，研究人员可能依赖人工样本数据来开发和测试他们的模型，然后再将其应用于实际患者数据。使用人工样本数据的一个显著优势是能够控制特定变量。通过生成具有已知特征的数据，研究人员可以创建在现实世界中难以或不可能复制的场景。这种受控环境允许对假设进行更严格的测试，并可能导致更可靠的结果。此外，人工样本数据可用于增强现有数据集，为机器学习模型提供更全面的训练数据。这在可用数据不平衡或缺乏多样性的情况下尤其有用。然而，使用人工样本数据也面临挑战。一个主要问题是过拟合的风险。当模型在人工样本数据上训练时，它可能学习到在真实数据中不存在的模式，从而导致在受控环境外表现不佳。为了减轻这种风险，尽可能使用真实数据对模型进行验证至关重要。此外，人工样本数据的质量至关重要；如果生成的数据不能准确反映现实世界的复杂性，从中得出的见解可能会产生误导。总之，尽管人工样本数据在推动研究和技术发展中发挥着至关重要的作用，但在使用时必须谨慎。研究人员必须确保理解依赖人工生成数据的局限性和潜在陷阱。通过将人工样本数据与真实世界观察相结合，我们可以开发出更强健的模型，并深入洞察我们希望理解的现象。随着技术的不断发展，生成和利用人工样本数据的方法可能会变得更加复杂，从而进一步增强我们解决各个领域复杂问题的能力。