corrected parameter

简明释义

修正参数

英英释义

例句

1.The simulation results were much better when we applied the corrected parameter 修正参数 to the equation.

当我们将修正参数应用到方程时，模拟结果要好得多。

2.The corrected parameter 修正参数 was essential for calibrating the instrument correctly.

修正参数对于正确校准仪器至关重要。

3.We need to update the corrected parameter 修正参数 in our software to reflect the latest findings.

我们需要在软件中更新修正参数，以反映最新的发现。

4.In our latest report, we included the corrected parameter 修正参数 values for better clarity.

在我们的最新报告中，我们包含了修正参数值以便于更清晰的理解。

5.After analyzing the data, we found that the corrected parameter 修正参数 significantly improved the model's accuracy.

在分析数据后，我们发现修正参数显著提高了模型的准确性。

作文

In the field of data analysis and machine learning, the concept of a corrected parameter plays a crucial role in improving the accuracy of models. A corrected parameter refers to a value that has been adjusted or modified based on new information or insights to better fit the underlying data or to enhance the performance of an algorithm. This adjustment is essential, especially when initial parameters are derived from estimates that may not fully capture the complexities of the data set. For instance, consider a scenario where a researcher is developing a predictive model to forecast sales for a retail business. Initially, they might set parameters based on historical sales data and market trends. However, as new data comes in—such as changes in consumer behavior due to economic shifts or seasonal variations—the original parameters may no longer be valid. To address this, the researcher would need to identify and implement corrected parameters that reflect these new realities. The process of correcting parameters typically involves various statistical techniques, including regression analysis, optimization algorithms, or machine learning methods. By utilizing these techniques, analysts can systematically adjust their parameters to minimize errors and improve the model's predictive power. For example, in a linear regression model, the coefficients (parameters) are often recalibrated through iterative methods to ensure that the model accurately captures the relationship between independent and dependent variables. Moreover, the importance of corrected parameters extends beyond just improving model accuracy. It also enhances the reliability of predictions and decisions made based on these models. In industries such as finance, healthcare, and engineering, having accurate and well-adjusted parameters can lead to significant cost savings, risk mitigation, and better resource allocation. For instance, in healthcare, predictive models that use corrected parameters can help in identifying patients at risk of certain conditions, allowing for timely interventions. However, it is essential to approach the process of parameter correction with caution. Overfitting is a common pitfall where a model becomes too tailored to the training data, resulting in poor generalization to unseen data. Therefore, while corrected parameters can significantly enhance model performance, it is vital to validate these adjustments using robust testing methodologies, such as cross-validation or out-of-sample testing. This ensures that the model remains effective across different scenarios and data sets. In conclusion, the concept of corrected parameters is integral to the practice of data analysis and modeling. By understanding and applying the principles of parameter correction, researchers and practitioners can develop more accurate, reliable, and actionable models. As the landscape of data continues to evolve, the ability to adapt and refine parameters will remain a key skill in ensuring the success of analytical endeavors.

在数据分析和机器学习领域，corrected parameter（修正参数）的概念在提高模型准确性方面发挥着至关重要的作用。corrected parameter指的是根据新信息或见解进行调整或修改的值，以更好地适应基础数据或增强算法的性能。这种调整是必要的，特别是当初始参数是基于可能未能充分捕捉数据集复杂性的估计得出的情况下。例如，考虑一个研究者开发预测模型以预测零售业务销售的场景。最初，他们可能根据历史销售数据和市场趋势设置参数。然而，随着新数据的出现——例如由于经济变化或季节性变化导致的消费者行为变化——原始参数可能不再有效。为了解决这个问题，研究者需要识别并实施反映这些新现实的corrected parameters（修正参数）。修正参数的过程通常涉及各种统计技术，包括回归分析、优化算法或机器学习方法。通过利用这些技术，分析师可以系统地调整他们的参数，以最小化误差并提高模型的预测能力。例如，在线性回归模型中，系数（参数）通常通过迭代方法重新校准，以确保模型准确捕捉自变量与因变量之间的关系。此外，corrected parameters（修正参数）的重要性不仅限于提高模型的准确性。它还增强了基于这些模型所做预测和决策的可靠性。在金融、医疗和工程等行业，拥有准确且经过良好调整的参数可以带来显著的成本节约、风险减轻和更好的资源分配。例如，在医疗保健中，使用corrected parameters（修正参数）的预测模型可以帮助识别某些疾病风险患者，从而允许及时干预。然而，处理参数修正的过程时必须谨慎。过拟合是一个常见的陷阱，即模型变得过于针对训练数据，导致对未见数据的泛化能力差。因此，尽管corrected parameters（修正参数）可以显著增强模型性能，但验证这些调整使用稳健测试方法（如交叉验证或样本外测试）至关重要。这确保模型在不同场景和数据集中的有效性。总之，corrected parameters（修正参数）的概念是数据分析和建模实践中不可或缺的一部分。通过理解和应用参数修正的原则，研究人员和从业者可以开发出更准确、可靠和可操作的模型。随着数据环境的不断发展，适应和完善参数的能力将继续成为确保分析工作成功的关键技能。