mean error

简明释义

平均误差

英英释义

例句

1.The model's performance was evaluated based on the mean error, which indicates the average difference between predicted and actual values.

模型的性能是基于均值误差进行评估的，这表示预测值与实际值之间的平均差异。

2.To improve accuracy, we need to minimize the mean error in our calculations.

为了提高准确性，我们需要最小化我们的计算中的均值误差。

3.In machine learning, understanding the mean error helps in tuning the algorithms effectively.

在机器学习中，理解均值误差有助于有效地调整算法。

4.The mean error of the forecasting model was found to be quite high, suggesting a need for adjustments.

预测模型的均值误差被发现相当高，这表明需要进行调整。

5.The mean error can be calculated by taking the average of all individual errors.

可以通过取所有个别误差的平均值来计算均值误差。

作文

In the realm of statistics and data analysis, understanding various metrics is crucial for interpreting results accurately. One such important metric is the mean error, which plays a significant role in assessing the accuracy of predictions made by models. The mean error is defined as the average of the differences between predicted values and actual values. This measure provides insight into how well a model performs by quantifying the errors in its predictions. To illustrate the concept of mean error, consider a simple example involving a weather forecasting model. Suppose the model predicts temperatures for a week, and the actual temperatures are recorded. By calculating the difference between the predicted and actual temperatures for each day, we can determine how far off the model was. If the forecasted temperatures were 20°C, 22°C, 21°C, 19°C, 23°C, 24°C, and 25°C, while the actual temperatures were 21°C, 22°C, 20°C, 18°C, 24°C, 23°C, and 26°C respectively, we can compute the daily errors: -1°C, 0°C, 1°C, 1°C, -1°C, 1°C, -1°C. Next, we sum these errors: -1 + 0 + 1 + 1 - 1 + 1 - 1 = 0. Finally, to find the mean error, we divide the total by the number of data points (in this case, 7). Therefore, the mean error is 0°C, indicating that on average, the model's predictions were accurate. However, it is essential to note that the mean error alone does not provide a complete picture of model performance. It can mask larger discrepancies in individual predictions. For instance, if some days had significantly larger errors, the mean error could still be low, suggesting a false sense of accuracy.This is where other metrics, such as the mean absolute error (MAE) and mean squared error (MSE), become valuable. Unlike mean error, MAE takes the absolute value of errors, thus preventing negative and positive errors from canceling each other out. MSE squares the errors before averaging them, which emphasizes larger errors even more. Both MAE and MSE provide a more nuanced understanding of model performance, especially when dealing with datasets that exhibit variability.In conclusion, while the mean error is a useful statistic for evaluating the accuracy of predictions, it should not be used in isolation. A comprehensive analysis of model performance requires considering multiple metrics to capture the full spectrum of prediction errors. By employing a combination of these measures, analysts can better understand the strengths and weaknesses of their models, ultimately leading to improved forecasting and decision-making processes. The careful interpretation of the mean error and its related metrics is essential for any data-driven endeavor, ensuring that conclusions drawn from data are both accurate and actionable.

在统计学和数据分析领域，理解各种指标对于准确解读结果至关重要。其中一个重要的指标是均值误差，它在评估模型预测准确性方面发挥着重要作用。均值误差被定义为预测值与实际值之间差异的平均值。这个指标通过量化预测中的错误，提供了模型表现的洞察。为了说明均值误差的概念，考虑一个涉及天气预报模型的简单例子。假设该模型预测一周的温度，并记录实际温度。通过计算每一天预测温度与实际温度之间的差异，我们可以确定模型的偏差。如果预测的温度分别为20°C、22°C、21°C、19°C、23°C、24°C和25°C，而实际温度分别为21°C、22°C、20°C、18°C、24°C、23°C和26°C，则我们可以计算每日误差：-1°C、0°C、1°C、1°C、-1°C、1°C、-1°C。接下来，我们将这些误差相加：-1 + 0 + 1 + 1 - 1 + 1 - 1 = 0。最后，为了找到均值误差，我们将总和除以数据点的数量（在这种情况下为7）。因此，均值误差为0°C，表明模型的预测在平均上是准确的。然而，需要注意的是，仅仅依靠均值误差并不能提供模型性能的完整图景。它可能掩盖个别预测中的较大差异。例如，如果某些天的误差显著较大，均值误差仍然可能较低，从而给人一种虚假的准确感。这就是其他指标，如均值绝对误差（MAE）和均方误差（MSE），变得有价值的地方。与均值误差不同，MAE取误差的绝对值，从而防止负误差和正误差相互抵消。MSE在平均之前对误差进行平方，这样更加强调较大的误差。MAE和MSE都提供了对模型性能更细致的理解，尤其是在处理具有变异性的数据库时。总之，虽然均值误差是评估预测准确性的有用统计量，但不应单独使用。全面分析模型性能需要考虑多个指标，以捕捉预测误差的全貌。通过采用这些指标的组合，分析人员可以更好地理解模型的优缺点，最终改善预测和决策过程。对均值误差及其相关指标的仔细解读对于任何数据驱动的工作至关重要，确保从数据中得出的结论既准确又可操作。