average error

简明释义

平均误差

英英释义

例句

1.We found that the average error 平均误差 in our data analysis was within acceptable limits.

我们发现数据分析中的平均误差 average error 在可接受范围内。

2.The model's performance improved significantly, with the average error 平均误差 dropping from 10% to 5%.

该模型的性能显著提高，平均误差 average error 从10%降至5%。

3.To evaluate the accuracy of our predictions, we calculated the average error 平均误差 across all test cases.

为了评估我们预测的准确性，我们计算了所有测试案例的平均误差 average error。

4.A lower average error 平均误差 indicates a more reliable forecasting model.

较低的平均误差 average error 表明预测模型更可靠。

5.By refining the algorithm, the team managed to reduce the average error 平均误差 by 15%.

通过优化算法，团队成功将平均误差 average error 降低了15%。

作文

In the field of statistics and data analysis, understanding the concept of average error is crucial for evaluating the accuracy of predictions and measurements. The term average error refers to the mean of the absolute differences between predicted values and the actual values in a dataset. This statistical measure provides insights into how closely a model or an estimation aligns with real-world observations. By calculating the average error, researchers and analysts can assess the performance of their models and make necessary adjustments to improve accuracy.For instance, consider a scenario where a meteorologist predicts the temperature for a week. If the actual temperatures are recorded, the meteorologist can compare these values to their predictions. The average error would be calculated by taking the absolute differences between each predicted temperature and the corresponding actual temperature, summing these differences, and dividing by the number of predictions. This process gives a single value that represents the average deviation from the actual temperatures.The importance of average error extends beyond meteorology. In various fields such as finance, engineering, and machine learning, it serves as a critical metric for evaluating the effectiveness of models. For example, in finance, if a model predicts stock prices, the average error can indicate how reliable the model's predictions are compared to actual market movements. A smaller average error signifies better predictive performance, while a larger one suggests that the model may need refinement.Moreover, the average error is particularly valuable because it is easy to interpret. Unlike some complex statistical measures that require advanced knowledge to understand, the average error provides a straightforward way to gauge accuracy. Stakeholders can quickly grasp whether a model is performing well or poorly based on this single metric.However, it is essential to recognize the limitations of using average error as a sole indicator of model performance. While it gives a general sense of accuracy, it does not account for the direction of the errors—whether the predictions are consistently overestimating or underestimating the actual values. Therefore, it is often beneficial to complement the average error with other metrics, such as root mean square error (RMSE) or mean absolute percentage error (MAPE), which provide more comprehensive insights into the model's performance.In conclusion, the concept of average error plays a significant role in various analytical domains. It helps professionals evaluate the accuracy of their predictions and refine their models accordingly. By understanding and utilizing the average error, analysts can enhance their decision-making processes and contribute to more reliable outcomes in their respective fields. As we continue to rely on data-driven decisions, mastering the interpretation of average error will remain a fundamental skill for anyone involved in data analysis and modeling.

在统计和数据分析领域，理解平均误差的概念对于评估预测和测量的准确性至关重要。术语平均误差指的是数据集中预测值与实际值之间绝对差异的均值。这一统计指标提供了模型或估计与现实观察之间的贴合程度的洞察。通过计算平均误差，研究人员和分析师可以评估其模型的性能，并做出必要的调整以提高准确性。例如，考虑一个气象学家预测一周温度的场景。如果记录了实际温度，气象学家可以将这些值与其预测进行比较。平均误差的计算方法是取每个预测温度与相应实际温度之间的绝对差异，求和后再除以预测的数量。这个过程给出了一个单一的值，代表了实际温度的平均偏差。平均误差的重要性超越了气象学。在金融、工程和机器学习等多个领域，它作为评估模型有效性的关键指标。例如，在金融领域，如果一个模型预测股票价格，平均误差可以表明该模型的预测与实际市场波动的可靠性。较小的平均误差表示更好的预测性能，而较大的则表明模型可能需要改进。此外，平均误差特别有价值，因为它易于解释。与一些复杂的统计指标相比，这些指标需要高级知识才能理解，平均误差提供了一种直接的方法来衡量准确性。利益相关者可以快速理解模型是否表现良好或较差，仅凭这一指标。然而，必须认识到，单独使用平均误差作为模型性能的唯一指标是有限制的。虽然它提供了准确性的一般感觉，但并未考虑误差的方向——预测是始终高估还是低估实际值。因此，通常最好将平均误差与其他指标（如均方根误差（RMSE）或平均绝对百分比误差（MAPE））结合使用，以提供对模型性能的更全面的洞察。总之，平均误差的概念在各个分析领域发挥着重要作用。它帮助专业人士评估预测的准确性并相应地完善模型。通过理解和利用平均误差，分析师可以增强其决策过程，并为各自领域的更可靠结果做出贡献。随着我们继续依赖数据驱动的决策，掌握平均误差的解释将仍然是任何参与数据分析和建模人员的基本技能。