aggregate error

简明释义

1. 集合误差; 2. 累积误差;

英英释义

例句

1.The model's performance improved after we reduced the aggregate error 总误差 by optimizing the parameters.

通过优化参数，我们减少了模型的aggregate error 总误差，从而提高了性能。

2.In our analysis, the aggregate error 总误差 was significantly lower than in previous tests.

在我们的分析中，aggregate error 总误差明显低于之前的测试。

3.The aggregate error 总误差 in the dataset indicates that there are issues with data quality.

数据集中的aggregate error 总误差表明数据质量存在问题。

4.After several iterations, the aggregate error 总误差 decreased, demonstrating the effectiveness of our model tuning.

经过几次迭代，aggregate error 总误差降低，证明了我们模型调优的有效性。

5.We need to calculate the aggregate error 总误差 to assess the overall accuracy of the system.

我们需要计算aggregate error 总误差以评估系统的整体准确性。

作文

In the realm of data analysis and statistical modeling, the term aggregate error refers to the total error that accumulates when combining the errors from individual observations or predictions. Understanding aggregate error is crucial for anyone involved in quantitative research, as it directly impacts the reliability and accuracy of the results obtained from data-driven studies. To illustrate this concept, let us consider a scenario where a researcher is analyzing the performance of students in a standardized test. Each student's score can be viewed as an individual observation, and the difference between their actual score and the predicted score based on various factors such as study habits, attendance, and socioeconomic status represents the individual error for each student. When these individual errors are summed up, we arrive at the aggregate error, which provides insight into the overall accuracy of the predictive model used.The significance of aggregate error becomes evident when we evaluate the effectiveness of different models or methodologies. For instance, if a researcher employs two different statistical models to predict student performance, they can compare the aggregate error of both models to determine which one offers better predictive accuracy. A lower aggregate error indicates that the model is more reliable and that its predictions are closer to the actual outcomes.Moreover, understanding aggregate error is essential in the field of machine learning. In machine learning algorithms, particularly in supervised learning, the goal is often to minimize the aggregate error during the training phase. This is achieved by adjusting the model parameters to reduce the difference between the predicted values and the actual target values across the entire dataset. The process of tuning a model to achieve the lowest possible aggregate error can significantly enhance its performance when applied to new, unseen data.In practical applications, recognizing the sources of aggregate error can lead to improvements in data collection and processing techniques. For example, if a significant portion of the aggregate error stems from measurement inaccuracies or data entry errors, implementing stricter quality control measures can help mitigate these issues. Additionally, identifying patterns in the aggregate error can provide valuable feedback for refining models and improving their predictive capabilities.In conclusion, the concept of aggregate error plays a pivotal role in data analysis, statistical modeling, and machine learning. By comprehensively understanding aggregate error, researchers and practitioners can make informed decisions about model selection, data collection, and strategies for improving accuracy. Ultimately, minimizing aggregate error is not just about enhancing statistical precision; it is also about ensuring that the insights derived from data serve their intended purpose effectively. Therefore, a robust grasp of aggregate error is indispensable for anyone aiming to excel in fields that rely heavily on data interpretation and analysis.

在数据分析和统计建模领域，术语aggregate error指的是当结合来自各个观察或预测的错误时所累积的总错误。理解aggregate error对于任何参与定量研究的人而言都至关重要，因为它直接影响到从数据驱动的研究中获得结果的可靠性和准确性。为了说明这一概念，让我们考虑一个场景：一位研究者正在分析学生在标准化测试中的表现。每个学生的成绩可以视为一个单独的观察，而他们的实际成绩与基于学习习惯、出勤率和社会经济状态等各种因素的预测成绩之间的差异则代表了每个学生的个体误差。当这些个体误差相加时，我们得到了aggregate error，这为我们提供了对所使用预测模型整体准确性的洞察。aggregate error的重要性在于评估不同模型或方法的有效性。例如，如果一位研究者采用两种不同的统计模型来预测学生表现，他们可以比较两个模型的aggregate error，以确定哪个模型提供了更好的预测准确性。较低的aggregate error表明该模型更可靠，其预测结果更接近实际结果。此外，理解aggregate error在机器学习领域也至关重要。在机器学习算法中，特别是在监督学习中，目标通常是在训练阶段最小化aggregate error。通过调整模型参数来减少整个数据集中预测值与实际目标值之间的差异，可以实现这一目标。为了达到最低可能的aggregate error，调整模型的过程可以显著增强其在应用于新数据时的表现。在实际应用中，识别aggregate error的来源可以促进数据收集和处理技术的改进。例如，如果aggregate error的大部分来源于测量不准确或数据录入错误，那么实施更严格的质量控制措施可以帮助减轻这些问题。此外，识别aggregate error中的模式可以为优化模型和提高其预测能力提供宝贵的反馈。总之，aggregate error的概念在数据分析、统计建模和机器学习中发挥着关键作用。通过全面理解aggregate error，研究人员和从业者可以就模型选择、数据收集和提高准确性的策略做出明智的决策。最终，最小化aggregate error不仅仅是为了提高统计精度；它还关乎确保从数据中得出的见解能够有效地服务于其预期目的。因此，深入掌握aggregate error对于任何希望在依赖数据解释和分析的领域中脱颖而出的人来说都是不可或缺的。