average error

简明释义

平均误差

英英释义

例句

1.The research team reported an average error 平均误差 of less than 5% in their findings.

研究小组报告称，他们的发现中平均误差 average error低于5%。

2.In our study, we calculated the average error 平均误差 of the model to evaluate its performance.

在我们的研究中，我们计算了模型的平均误差 average error以评估其性能。

3.The average error 平均误差 in the weather predictions was surprisingly low this month.

这个月天气预报的平均误差 average error出乎意料地低。

4.After several iterations, the average error 平均误差 of our predictions decreased significantly.

经过几次迭代后，我们预测的平均误差 average error显著下降。

5.To improve our algorithm, we need to minimize the average error 平均误差 across all test cases.

为了改进我们的算法，我们需要减少所有测试用例的平均误差 average error。

作文

In the field of data analysis and statistics, 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 actual values. It provides a straightforward way to assess how far off a model or measurement is from the true value on average. For instance, if a weather forecasting model predicts temperatures for a week, the average error would help us understand how close those predictions are to the actual recorded temperatures.To calculate the average error, one must first determine the errors for each prediction. This is done by subtracting the predicted value from the actual value, taking the absolute value of that difference to ensure that all errors are treated as positive numbers. After calculating the absolute errors, you sum them up and divide by the number of predictions made. This gives you the average error, which can be expressed mathematically as:Average Error = (|Error1| + |Error2| + ... + |ErrorN|) / NWhere ErrorN represents the individual errors, and N is the total number of predictions. The significance of the average error lies in its ability to provide a clear and concise measure of accuracy. For instance, if a model has an average error of 2 degrees Celsius in predicting temperature, it indicates that, on average, the model's predictions deviate from the actual temperatures by 2 degrees. This information can be invaluable for decision-making processes, especially in fields such as meteorology, finance, and engineering, where precise measurements are essential.Moreover, the average error can serve as a benchmark for comparing different models. By examining the average error of various predictive models, analysts can determine which model performs better in terms of accuracy. A lower average error indicates a more reliable model, while a higher average error suggests that the model may need adjustments or improvements.However, it is important to note that the average error does have its limitations. One major drawback is that it does not account for the direction of the errors. For example, a model might consistently overestimate or underestimate values, leading to a situation where the average error appears small, but the model may still be systematically inaccurate. In such cases, it may be beneficial to consider other metrics, such as the mean squared error (MSE) or the root mean squared error (RMSE), which provide additional insights into the performance of the model.In conclusion, the concept of average error is fundamental in assessing the performance of predictive models and measurements. It offers a simple yet effective way to quantify accuracy, enabling analysts to make informed decisions based on the reliability of their data. Understanding how to compute and interpret the average error is essential for anyone involved in data analysis, as it aids in refining models and improving overall accuracy in various fields. By being aware of both its strengths and limitations, practitioners can leverage the average error effectively to enhance their analytical capabilities.

在数据分析和统计学领域，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预测值与实际值之间绝对差异的平均值。它提供了一种直接的方法来评估模型或测量值与真实值的平均偏差。例如，如果天气预报模型预测一周的温度，平均误差将帮助我们了解这些预测与实际记录的温度有多接近。计算平均误差，首先必须确定每个预测的误差。这是通过将预测值减去实际值，然后取该差值的绝对值来完成的，以确保所有误差都被视为正数。在计算出绝对误差后，将它们相加并除以所做预测的数量。这将给出平均误差，可以用数学公式表示为：平均误差 = (|误差1| + |误差2| + ... + |误差N|) / N其中误差N表示单个误差，N是预测总数。平均误差的重要性在于它能够提供清晰简明的准确性衡量。例如，如果一个模型在预测温度时的平均误差为2摄氏度，这表明该模型的预测与实际温度的偏差平均为2度。这些信息对于决策过程至关重要，尤其是在气象、金融和工程等领域，精确的测量至关重要。此外，平均误差还可以作为比较不同模型的基准。通过检查各种预测模型的平均误差，分析师可以确定哪个模型在准确性方面表现更好。较低的平均误差表明模型更可靠，而较高的平均误差则表明模型可能需要调整或改进。然而，需要注意的是，平均误差确实存在一些局限性。一个主要缺点是它不考虑误差的方向。例如，一个模型可能持续高估或低估值，导致平均误差看起来很小，但模型仍然可能系统性不准确。在这种情况下，考虑其他指标，如均方误差（MSE）或均方根误差（RMSE），可能会更有益，因为这些指标提供了关于模型性能的额外见解。总之，平均误差的概念在评估预测模型和测量性能中是基础。它提供了一种简单有效的方式来量化准确性，使分析师能够根据数据的可靠性做出明智的决策。理解如何计算和解释平均误差对于任何参与数据分析的人来说都是必不可少的，因为它有助于完善模型并提高各个领域的整体准确性。通过意识到其优缺点，实践者可以有效利用平均误差来增强其分析能力。