geometric mean error

简明释义

几何平均误差

英英释义

例句

1.Using the geometric mean error allows for a more robust analysis of the data set with outliers.

使用几何平均误差可以对包含异常值的数据集进行更稳健的分析。

2.The geometric mean error 几何平均误差 is particularly useful in machine learning when comparing models with different outputs.

在机器学习中，当比较输出不同的模型时，几何平均误差特别有用。

3.The researchers reported a geometric mean error of 2.5% in their climate model simulations.

研究人员报告他们的气候模型模拟中的几何平均误差为2.5%。

4.The model's performance was evaluated using the geometric mean error, which provides a more balanced view of accuracy across different scales.

该模型的性能评估使用了几何平均误差，这提供了对不同尺度上准确性更平衡的视角。

5.In financial forecasting, the geometric mean error 几何平均误差 helps to assess the accuracy of predictions over time.

在财务预测中，几何平均误差有助于评估预测随时间的准确性。

作文

In the realm of statistics and data analysis, understanding various error metrics is crucial for evaluating the accuracy of predictions and models. One such metric that stands out due to its unique properties is the geometric mean error. This term refers to a method of calculating the average error of a set of values by taking the geometric mean of their absolute errors. Unlike the arithmetic mean, which can be heavily influenced by outliers, the geometric mean error provides a more balanced perspective, especially in datasets that exhibit skewness or variability.To comprehend the significance of the geometric mean error, it is essential to first grasp the concept of geometric mean itself. The geometric mean is calculated by multiplying all the numbers in a dataset together and then taking the nth root of the resulting product, where n is the total number of values. This approach is particularly useful when dealing with ratios or percentages, as it mitigates the impact of extreme values. Thus, when we apply this to error measurement, the geometric mean error becomes a powerful tool for assessing the performance of predictive models.For example, consider a scenario where a company is forecasting sales figures for the upcoming quarter. If the forecasted values deviate significantly from the actual sales, it is crucial to quantify this deviation accurately. By calculating the geometric mean error, the company can obtain a clearer picture of how well their model performs across different scenarios without being skewed by any particularly poor predictions.Moreover, the geometric mean error is especially beneficial in fields such as finance, environmental science, and engineering, where data often spans several orders of magnitude. In these cases, using the geometric mean error allows analysts to evaluate model accuracy on a relative scale, making it easier to compare results across various datasets.However, it is important to note that while the geometric mean error has its advantages, it is not without limitations. For instance, it cannot handle negative values since the geometric mean is undefined for negative numbers. Therefore, when applying this metric, one must ensure that the data being analyzed is appropriate for geometric calculation. Additionally, the geometric mean error may not always reflect the true nature of errors in certain contexts, particularly when the distribution of errors is not log-normal.In conclusion, the geometric mean error is a valuable statistical measure that provides insights into the accuracy of predictions while reducing the influence of outliers. Its ability to present a more nuanced view of error metrics makes it an essential tool for data analysts and researchers. By incorporating the geometric mean error into their evaluation processes, professionals can make more informed decisions and enhance the reliability of their models. As data continues to play a pivotal role in various industries, mastering metrics like the geometric mean error will undoubtedly contribute to more effective data-driven strategies.

在统计学和数据分析领域，理解各种误差度量对于评估预测和模型的准确性至关重要。其中一个因其独特属性而脱颖而出的度量是几何平均误差。这个术语指的是通过对一组值的绝对误差取几何平均来计算平均误差的方法。与算术平均数不同，算术平均数可能会受到极端值的强烈影响，而几何平均误差则提供了更平衡的视角，尤其是在数据集表现出偏斜或变异性时。要理解几何平均误差的重要性，首先必须掌握几何平均的概念。几何平均是通过将数据集中所有数字相乘，然后取结果的n次方根来计算的，其中n是值的总数。这种方法在处理比率或百分比时特别有用，因为它减轻了极端值的影响。因此，当我们将其应用于误差测量时，几何平均误差成为评估预测模型性能的强大工具。例如，考虑一种情况，一家公司正在预测即将到来的季度销售数字。如果预测值与实际销售有显著偏差，准确量化这种偏差至关重要。通过计算几何平均误差，公司可以获得更清晰的模型在不同场景下表现的图景，而不受任何特别糟糕预测的影响。此外，几何平均误差在金融、环境科学和工程等领域尤其有益，这些领域的数据通常跨越几个数量级。在这些情况下，使用几何平均误差使分析师能够在相对规模上评估模型的准确性，从而更容易比较不同数据集的结果。然而，重要的是要注意，尽管几何平均误差有其优点，但也并非没有局限性。例如，它无法处理负值，因为几何平均对负数是未定义的。因此，在应用这一度量时，必须确保所分析的数据适合几何计算。此外，几何平均误差在某些情况下可能无法真实反映误差的本质，特别是当误差的分布不是对数正态分布时。总之，几何平均误差是一种有价值的统计度量，它提供了对预测准确性的洞察，同时减少了极端值的影响。它呈现出更细致的误差度量视角，使其成为数据分析师和研究人员的重要工具。通过将几何平均误差纳入评估过程中，专业人士能够做出更明智的决策，并增强其模型的可靠性。随着数据在各个行业中发挥着关键作用，掌握像几何平均误差这样的度量无疑将有助于更有效的数据驱动策略。