geometric mean error

简明释义

N.

英英释义

例句

1.The geometric mean error can be particularly useful when dealing with data that spans several orders of magnitude.

当处理跨越多个数量级的数据时，几何平均误差特别有用。

2.In forecasting, minimizing the geometric mean error is crucial for improving prediction reliability.

在预测中，最小化几何平均误差对提高预测可靠性至关重要。

3.Researchers often prefer the geometric mean error over the arithmetic mean error in their analyses.

研究人员在分析中通常更喜欢使用几何平均误差而不是算术平均误差。

4.The model's performance was evaluated using the geometric mean error, which indicates its accuracy across different datasets.

该模型的性能通过几何平均误差进行评估，这表明其在不同数据集上的准确性。

5.To assess the effectiveness of the new algorithm, we calculated the geometric mean error for various test cases.

为了评估新算法的有效性，我们计算了各种测试案例的几何平均误差。

作文

In the field of data analysis and statistics, the accuracy of predictions and measurements is paramount. One of the essential metrics used to evaluate the performance of predictive models is the geometric mean error. This term refers to a statistical measure that calculates the average of a set of values by taking the geometric mean of the errors between predicted and actual values. Unlike the arithmetic mean, which can be heavily influenced by outliers, the geometric mean error provides a more balanced view of the error distribution, making it particularly useful in fields where multiplicative factors are involved.The geometric mean error is especially significant in areas such as finance, economics, and environmental studies, where data can vary greatly in scale. For instance, when predicting stock prices or economic indicators, using the geometric mean error allows analysts to understand the average percentage error relative to the actual values, providing insights into the model's performance across different scenarios.To calculate the geometric mean error, one first needs to determine the individual errors by subtracting the predicted values from the actual values. These errors are then expressed as ratios of the predicted values, allowing for a comparison that accounts for the scale of the data. The next step involves taking the product of these ratios and raising it to the power of one divided by the number of observations. This results in the geometric mean of the errors, which is then often multiplied by 100 to express it as a percentage. This process highlights the central tendency of the errors in a way that is less sensitive to extreme values.Understanding the geometric mean error is crucial for practitioners who aim to refine their predictive models. By focusing on this metric, they can identify whether their models consistently overestimate or underestimate values. This feedback loop is essential for model improvement, as it guides adjustments to algorithms and methodologies used in forecasting.Moreover, the geometric mean error can also facilitate comparisons between different models. When evaluating multiple predictive approaches, analysts can use this metric to determine which model yields the lowest average error in a multiplicative context. This comparative analysis can lead to better decision-making, as stakeholders can select models that provide more reliable forecasts based on the geometric mean error findings.In conclusion, the geometric mean error is a vital statistic in the realm of data analysis, offering a nuanced perspective on prediction accuracy. Its ability to mitigate the influence of outliers makes it a preferred choice for many analysts and researchers. As we continue to rely on data-driven decisions in various sectors, understanding and applying the geometric mean error will undoubtedly enhance our capabilities in generating accurate and actionable insights.

在数据分析和统计学领域，预测和测量的准确性至关重要。评估预测模型性能的基本指标之一是几何平均误差。这个术语指的是一种统计度量，通过计算预测值与实际值之间误差的几何平均值来评估一组值的平均水平。与算术平均数不同，后者可能会受到异常值的严重影响，几何平均误差提供了对误差分布更为平衡的视角，这使得它在涉及乘法因子的领域中尤为有用。几何平均误差在金融、经济和环境研究等领域尤其重要，因为这些领域的数据规模差异很大。例如，在预测股票价格或经济指标时，使用几何平均误差可以让分析师理解相对于实际值的平均百分比误差，从而提供关于模型在不同场景下表现的洞察。要计算几何平均误差，首先需要通过将预测值从实际值中减去来确定个体误差。这些误差随后以预测值的比率形式表达，从而允许进行规模数据的比较。下一步是将这些比率的乘积提升到观察数量的倒数的幂。这将产生误差的几何平均值，通常再乘以100以表示为百分比。这个过程突出了误差的集中趋势，以一种较不敏感于极端值的方式。理解几何平均误差对于希望优化其预测模型的从业者至关重要。通过关注这一指标，他们可以识别出模型是否持续高估或低估值。这种反馈循环对于模型改进至关重要，因为它指导调整用于预测的算法和方法。此外，几何平均误差还可以促进不同模型之间的比较。在评估多个预测方法时，分析师可以使用这一指标来确定哪个模型在乘法上下文中产生最低的平均误差。这种比较分析可以促进更好的决策，因为利益相关者可以基于几何平均误差的发现选择提供更可靠预测的模型。总之，几何平均误差是数据分析领域的重要统计数据，提供了对预测准确性的细致视角。它能够减轻异常值的影响，使其成为许多分析师和研究人员的首选。随着我们在各个行业继续依赖数据驱动的决策，理解和应用几何平均误差无疑将增强我们在生成准确和可操作的洞察方面的能力。