average error

简明释义

平均误差

英英释义

例句

1.The researchers reported an average error 平均误差 of 0.2 units in their measurements.

研究人员报告称，他们的测量结果的平均误差 average error 为0.2单位。

2.During the analysis, we calculated the average error 平均误差 of our predictions to assess accuracy.

在分析过程中，我们计算了预测的平均误差 average error 以评估准确性。

3.By fine-tuning the algorithm, we managed to decrease the average error 平均误差 from 10% to 3%.

通过微调算法，我们成功将平均误差 average error 从10%降低到3%。

4.The average error 平均误差 in the survey results was surprisingly low, indicating high consistency among responses.

调查结果的平均误差 average error 出乎意料地低，表明回答之间的一致性很高。

5.The model's performance improved significantly, reducing the average error 平均误差 to less than 5%.

模型的性能显著提高，平均误差 average error 降低到5%以下。

作文

In the field of statistics and data analysis, the concept of average error plays a crucial role in assessing the accuracy of predictions or measurements. The average error refers to the mean of the absolute differences between predicted values and the actual values. It serves as a valuable tool for researchers and analysts to determine how close their models are to reality. Understanding this concept is essential for anyone involved in quantitative research, as it provides insights into the reliability of data-driven conclusions.To illustrate the importance of average error, consider a scenario where a meteorologist predicts the temperature for a given day. If the predicted temperature is 75°F, but the actual temperature turns out to be 70°F, the error in this prediction is 5°F. If the meteorologist makes several predictions throughout the week, calculating the average error will help them understand their forecasting accuracy over time. By averaging the errors from each day, they can identify patterns and improve their predictive models.Calculating the average error involves several steps. First, one must collect the data points, which include both the predicted and actual values. Next, the absolute errors are computed by taking the absolute difference between each predicted value and its corresponding actual value. Finally, these absolute errors are summed up and divided by the number of observations to obtain the average error. This process not only provides a single metric that summarizes the overall performance of the predictions but also highlights specific areas where improvements can be made.One of the advantages of using average error is its straightforward interpretation. A lower average error indicates that the predictions are more accurate, while a higher average error suggests that there is a significant discrepancy between predicted and actual values. This clarity is particularly useful when presenting findings to stakeholders who may not have a statistical background. They can easily grasp the implications of the average error without delving into complex statistical jargon.However, it is important to note that average error has its limitations. For instance, it does not account for the direction of errors—whether the predictions tend to be consistently overestimated or underestimated. In some cases, using the root mean square error (RMSE) or mean absolute percentage error (MAPE) may provide a more comprehensive view of prediction accuracy. These alternative metrics incorporate additional information that can lead to better decision-making.In conclusion, the concept of average error is fundamental in evaluating the performance of predictive models across various fields, including finance, healthcare, and environmental science. By quantifying the discrepancies between predicted and actual values, analysts can refine their methods and enhance the reliability of their forecasts. As data continues to play an increasingly significant role in our lives, mastering the use of average error and its implications will be invaluable for anyone working with data-driven decisions. Ultimately, understanding and applying the average error can lead to more informed choices and improved outcomes in numerous domains.

在统计学和数据分析领域，平均误差的概念在评估预测或测量的准确性方面发挥着至关重要的作用。平均误差是指预测值与实际值之间绝对差值的均值。它为研究人员和分析师提供了一个有价值的工具，以确定他们的模型与现实的接近程度。理解这一概念对于任何参与定量研究的人来说都是必不可少的，因为它提供了对数据驱动结论可靠性的洞察。为了说明平均误差的重要性，考虑一个气象学家的场景，他预测某一天的温度。如果预测的温度是75°F，但实际温度为70°F，则该预测的误差为5°F。如果气象学家在一周内进行了多次预测，计算平均误差将帮助他们理解其预测的准确性。通过对每一天的误差进行平均，他们可以识别出模式并改进其预测模型。计算平均误差涉及几个步骤。首先，必须收集数据点，包括预测值和实际值。接下来，通过计算每个预测值与其对应的实际值之间的绝对差来计算绝对误差。最后，将这些绝对误差相加并除以观察值的数量，以获得平均误差。这个过程不仅提供了一个总结预测整体表现的单一指标，还突出了可以改进的特定领域。使用平均误差的一个优点是其直观的解释。较低的平均误差表明预测更准确，而较高的平均误差则表明预测值与实际值之间存在显著差异。这种清晰性在向可能没有统计背景的利益相关者展示发现时尤其有用。他们可以轻松理解平均误差的含义，而无需深入复杂的统计术语。然而，重要的是要注意，平均误差也有其局限性。例如，它不考虑误差的方向——无论预测值是倾向于持续高估还是低估。在某些情况下，使用均方根误差（RMSE）或平均绝对百分比误差（MAPE）可能会提供更全面的预测准确性视图。这些替代指标包含额外的信息，可以导致更好的决策。总之，平均误差的概念在各个领域评估预测模型的性能中是基本的，包括金融、医疗保健和环境科学。通过量化预测值与实际值之间的差异，分析师可以精炼他们的方法，提高其预测的可靠性。随着数据在我们生活中扮演着越来越重要的角色，掌握平均误差的使用及其影响，对于任何处理数据驱动决策的人来说都将是无价的。最终，理解和应用平均误差可以在众多领域带来更明智的选择和改善的结果。