average error

简明释义

平均误差

英英释义

例句

1.The average error 平均误差 in our calculations was within acceptable limits.

我们计算中的平均误差 average error在可接受的范围内。

2.After several iterations, the average error 平均误差 dropped to an all-time low.

经过几次迭代，平均误差 average error降到了历史最低点。

3.The model's performance improved significantly after reducing the average error 平均误差 by 15%.

在将平均误差 average error减少了15%后，模型的性能显著提高。

4.We calculated the average error 平均误差 to assess the accuracy of our predictions.

我们计算了平均误差 average error以评估我们预测的准确性。

5.To improve the algorithm, we need to analyze the average error 平均误差 across different datasets.

为了改进算法，我们需要分析不同数据集的平均误差 average error。

作文

In the realm of data analysis and statistics, the concept of average error plays a crucial role in understanding the accuracy of predictions and measurements. The average error is defined as the average of the differences between predicted values and actual values, providing a clear picture of how well a model or method performs. By calculating the average error, researchers can assess the reliability of their findings and make necessary adjustments to improve their models.To illustrate the importance of average error, let’s consider a simple example involving temperature predictions. Suppose a meteorologist predicts the temperature for a week, and the actual temperatures recorded are slightly different from the predictions. By calculating the average error, the meteorologist can determine how accurate their forecasts were over that period. If the average error is low, it indicates that the predictions were close to the actual values, while a high average error suggests significant discrepancies.The calculation of average error involves a straightforward mathematical approach. First, one must find the difference between each predicted value and its corresponding actual value. Then, these differences are summed up, and the total is divided by the number of observations. This process yields the average error, which serves as a valuable metric for evaluating performance.Moreover, the average error can be a useful tool in various fields beyond meteorology. For instance, in finance, investors often rely on predictive models to forecast stock prices. By analyzing the average error of these predictions, they can gauge the effectiveness of their investment strategies. A lower average error may indicate a more reliable model, leading to better investment decisions.In engineering, the average error is equally significant. Engineers frequently utilize models to predict the behavior of structures under various conditions. By assessing the average error of their predictions against real-world measurements, they can refine their designs and ensure safety and efficiency.However, it is important to note that while the average error provides valuable insights, it does not capture all aspects of prediction accuracy. For instance, it does not account for the direction of the errors; a model may consistently overestimate or underestimate values, leading to a low average error despite poor performance in practice. Therefore, it is often beneficial to complement the average error with other metrics, such as the mean absolute error (MAE) or root mean square error (RMSE), to gain a more comprehensive understanding of model performance.In conclusion, the average error is an essential metric in data analysis, enabling researchers and practitioners to evaluate the accuracy of their predictions. By understanding and calculating the average error, individuals can make informed decisions across various fields, from meteorology to finance and engineering. As we continue to rely on data-driven approaches in our increasingly complex world, mastering the concept of average error will undoubtedly enhance our ability to interpret and utilize data effectively.

在数据分析和统计学领域，平均误差的概念在理解预测和测量的准确性方面起着至关重要的作用。平均误差被定义为预测值与实际值之间差异的平均值，提供了模型或方法性能的清晰图景。通过计算平均误差，研究人员可以评估其发现的可靠性，并进行必要的调整以改善其模型。为了说明平均误差的重要性，让我们考虑一个简单的例子，涉及温度预测。假设一位气象学家预测了一周的温度，而实际记录的温度与预测值略有不同。通过计算平均误差，气象学家可以确定他们的预测在该期间的准确性。如果平均误差较低，则表明预测值接近实际值，而较高的平均误差则表明存在显著差异。平均误差的计算涉及一种简单的数学方法。首先，必须找出每个预测值与其相应实际值之间的差异。然后，将这些差异相加，并将总和除以观察次数。这一过程得出的就是平均误差，它作为评估性能的有价值指标。此外，平均误差在气象学之外的多个领域也同样有用。例如，在金融领域，投资者通常依赖预测模型来预测股票价格。通过分析这些预测的平均误差，他们可以评估投资策略的有效性。较低的平均误差可能表明模型更可靠，从而导致更好的投资决策。在工程学中，平均误差同样重要。工程师经常利用模型来预测结构在各种条件下的行为。通过评估其预测与现实世界测量结果的平均误差，他们可以优化设计，确保安全和效率。然而，需要注意的是，尽管平均误差提供了有价值的见解，但它并不能捕捉预测准确性的所有方面。例如，它不考虑误差的方向；一个模型可能会持续高估或低估值，从而导致较低的平均误差，尽管在实际应用中表现不佳。因此，通常建议将平均误差与其他指标（如平均绝对误差（MAE）或均方根误差（RMSE））结合使用，以获得对模型性能的更全面理解。总之，平均误差是数据分析中的一个基本指标，使研究人员和从业者能够评估其预测的准确性。通过理解和计算平均误差，个人可以在气象学、金融和工程等各个领域做出明智的决策。随着我们在日益复杂的世界中继续依赖数据驱动的方法，掌握平均误差的概念无疑将增强我们有效解释和利用数据的能力。