arithmetical mean error

简明释义

算术平均误差

英英释义

例句

1.The report indicated that the arithmetical mean error 算术平均误差 was lower than expected, suggesting a reliable forecasting method.

报告指出算术平均误差低于预期，这表明预测方法可靠。

2.The data analyst calculated the arithmetical mean error 算术平均误差 to assess the accuracy of the predictive model.

数据分析师计算了算术平均误差来评估预测模型的准确性。

3.The arithmetical mean error 算术平均误差 can provide insights into the consistency of the measurements taken.

算术平均误差可以提供对所测量的一致性的见解。

4.In machine learning, minimizing the arithmetical mean error 算术平均误差 is crucial for improving model performance.

在机器学习中，最小化算术平均误差对于提高模型性能至关重要。

5.To validate the results, we compared the arithmetical mean error 算术平均误差 of different algorithms.

为了验证结果，我们比较了不同算法的算术平均误差。

作文

In the field of statistics and data analysis, understanding the concept of arithmetical mean error is crucial for interpreting the accuracy of predictions and measurements. The term refers to the average of the absolute differences between predicted values and actual values. This metric is particularly useful in various applications, such as forecasting, quality control, and scientific research, where precision is paramount. To illustrate, consider a scenario in which a meteorologist predicts the temperature for a week. If the predicted temperatures are consistently higher or lower than the actual temperatures, the arithmetical mean error provides a quantitative measure of this discrepancy. By calculating the average of these errors, the meteorologist can assess the reliability of their forecasts and make necessary adjustments for future predictions.The formula for calculating arithmetical mean error is straightforward: first, you subtract the predicted value from the actual value to find the error for each observation. Then, you take the absolute value of each error to eliminate negative values. Finally, you sum all the absolute errors and divide by the number of observations. This process yields a single value that represents the average error across all predictions, providing a clear picture of the model's performance.Understanding arithmetical mean error is not only important for meteorologists but also for professionals in various fields, including economics, engineering, and healthcare. For instance, in economics, analysts may use this metric to evaluate the accuracy of economic models that predict market trends. In engineering, it can help in assessing the precision of manufacturing processes, ensuring that products meet quality standards. In healthcare, researchers might utilize arithmetical mean error when analyzing the effectiveness of treatment protocols, comparing expected patient outcomes with actual results.Moreover, the arithmetical mean error is often used in conjunction with other statistical measures, such as the root mean square error (RMSE) and mean absolute percentage error (MAPE). While arithmetical mean error focuses solely on the magnitude of errors, RMSE takes into account the square of the errors, giving more weight to larger discrepancies. This can be particularly useful when large errors are more significant than small ones. On the other hand, MAPE expresses the error as a percentage of the actual values, making it easier to interpret in relative terms.In conclusion, the arithmetical mean error serves as an essential tool for evaluating the accuracy of predictions across various disciplines. By providing a clear and quantifiable measure of error, it enables professionals to refine their models and improve their decision-making processes. Whether in meteorology, economics, engineering, or healthcare, understanding and utilizing arithmetical mean error can lead to better outcomes and more reliable analyses. As data continues to play a pivotal role in our world, mastering this concept will undoubtedly enhance our ability to interpret and act upon information effectively.

在统计学和数据分析领域，理解算术平均误差的概念对于解释预测和测量的准确性至关重要。这个术语指的是预测值与实际值之间绝对差异的平均值。这个指标在各种应用中尤其有用，例如预测、质量控制和科学研究，其中精确度至关重要。举个例子，考虑一个气象学家预测一周的温度的场景。如果预测的温度始终高于或低于实际温度，算术平均误差提供了这种差异的定量衡量。通过计算这些误差的平均值，气象学家可以评估他们预测的可靠性，并为未来的预测做必要的调整。计算算术平均误差的公式很简单：首先，从实际值中减去预测值以找到每个观测值的误差。然后，取每个误差的绝对值，以消除负值。最后，将所有绝对误差相加并除以观测值的数量。这一过程产生一个单一的值，表示所有预测的平均误差，提供了模型性能的清晰图景。理解算术平均误差不仅对气象学家重要，而且对经济学、工程学和医疗保健等各个领域的专业人士也同样重要。例如，在经济学中，分析师可能会使用这个指标来评估预测市场趋势的经济模型的准确性。在工程学中，它可以帮助评估制造过程的精度，确保产品符合质量标准。在医疗保健中，研究人员可能会在分析治疗方案的有效性时利用算术平均误差，比较预期的患者结果与实际结果。此外，算术平均误差通常与其他统计指标结合使用，例如均方根误差（RMSE）和平均绝对百分比误差（MAPE）。虽然算术平均误差仅关注误差的大小，但RMSE考虑了误差的平方，从而对较大的差异给予更多权重。这在较大误差比小误差更重要时特别有用。另一方面，MAPE将误差表示为实际值的百分比，使其在相对意义上更易于解释。总之，算术平均误差作为评估各个学科预测准确性的基本工具。通过提供清晰且可量化的误差衡量，它使专业人士能够完善他们的模型并改善决策过程。无论是在气象学、经济学、工程学还是医疗保健领域，理解和利用算术平均误差都能带来更好的结果和更可靠的分析。随着数据在我们生活中日益重要，掌握这一概念无疑将增强我们有效解读和处理信息的能力。