median error

简明释义

中间误差

英英释义

例句

1.The model's performance was evaluated using the median error to ensure it wasn't skewed by outliers.

该模型的性能通过使用中位数误差进行评估，以确保不受异常值的影响。

2.The median error helps to provide a clearer picture of typical performance compared to average errors.

与平均误差相比，中位数误差有助于提供更清晰的典型性能图像。

3.By minimizing the median error, the team improved the overall reliability of their forecasting model.

通过最小化中位数误差，团队提高了其预测模型的整体可靠性。

4.In our analysis, we found that the median error was significantly lower than the mean error, indicating a robust model.

在我们的分析中，我们发现中位数误差显著低于平均误差，表明模型稳健。

5.To assess the accuracy of the predictions, we calculated the median error across all test samples.

为评估预测的准确性，我们计算了所有测试样本的中位数误差。

作文

In the realm of statistics and data analysis, understanding various measures of error is crucial for interpreting results accurately. One such measure is the median error, which plays a significant role in assessing the accuracy of predictions or estimations. The median error refers to the median of the differences between predicted values and actual values. It provides a robust metric that is less sensitive to outliers compared to other measures, such as mean error. This characteristic makes the median error particularly useful in fields where extreme values can skew results, such as economics, meteorology, or machine learning.To illustrate the importance of the median error, let us consider an example from the field of weather forecasting. Suppose a meteorologist predicts the temperature for a week, and the actual temperatures are recorded. If we calculate the differences between the predicted and actual temperatures, we might find some large discrepancies on certain days due to sudden weather changes. These discrepancies could significantly affect the mean error calculation, making it appear that the forecasts were less accurate than they truly are. However, by calculating the median error, we can obtain a more reliable measure of forecast accuracy, as it focuses on the middle value of the errors rather than being influenced by the extremes.Moreover, the median error can also help in evaluating the performance of machine learning models. When training a model, one often seeks to minimize prediction errors. By examining the median error, data scientists can understand how well their model performs across different scenarios, especially when dealing with imbalanced datasets where some classes may have much fewer samples than others. The median error allows them to gauge the model's effectiveness without the misleading influence of outliers, ensuring that the evaluation reflects the model's true predictive power.In addition to its practical applications, the median error also has theoretical implications. It is a part of a broader family of statistical measures that help analysts make informed decisions based on data. Understanding the characteristics of the median error enables researchers and practitioners to choose the appropriate metrics for their analyses, depending on the nature of their data and the specific questions they seek to answer.In conclusion, the median error is a vital concept in statistics that aids in the evaluation of accuracy in various fields. Its robustness against outliers and its ability to provide a clearer picture of prediction performance make it an essential tool for data analysts and researchers alike. As we continue to navigate an increasingly data-driven world, grasping the significance of the median error will undoubtedly enhance our analytical capabilities and improve decision-making processes across numerous domains.

在统计学和数据分析领域，理解各种误差度量对于准确解读结果至关重要。其中一个重要的度量是中位数误差，它在评估预测或估计的准确性方面发挥着重要作用。中位数误差指的是预测值与实际值之间差异的中位数。与其他度量（如均值误差）相比，它提供了一种稳健的指标，对异常值的敏感性较低。这一特性使得中位数误差在一些极端值可能扭曲结果的领域（如经济学、气象学或机器学习）中特别有用。为了说明中位数误差的重要性，让我们考虑一个来自天气预报领域的例子。假设气象学家预测一周的温度，并记录实际温度。如果我们计算预测温度与实际温度之间的差异，我们可能会发现某些天由于突发天气变化而存在较大的差异。这些差异可能会显著影响均值误差的计算，使得预报看起来比实际情况要不准确。然而，通过计算中位数误差，我们可以获得一个更可靠的预测准确性度量，因为它关注的是误差的中间值，而不是受到极端值的影响。此外，中位数误差也可以帮助评估机器学习模型的性能。在训练模型时，人们通常希望最小化预测误差。通过检查中位数误差，数据科学家可以了解他们的模型在不同场景下的表现，尤其是在处理不平衡数据集时，其中某些类别的样本数量可能远少于其他类别。中位数误差使他们能够在不受异常值误导的情况下评估模型的有效性，确保评估反映出模型的真实预测能力。除了其实际应用外，中位数误差还具有理论意义。它是统计度量的更广泛家族的一部分，帮助分析师根据数据做出明智的决策。理解中位数误差的特征使研究人员和从业者能够根据数据的性质和他们寻求回答的具体问题选择适当的度量。总之，中位数误差是统计学中的一个重要概念，有助于评估各个领域的准确性。它对异常值的鲁棒性以及提供更清晰的预测性能图景的能力，使其成为数据分析师和研究人员的重要工具。随着我们继续在一个日益以数据驱动的世界中航行，掌握中位数误差的意义无疑将增强我们的分析能力，并改善各个领域的决策过程。