mean error

简明释义

平均误差

英英释义

例句

1.In our analysis, we found that the mean error was significantly lower after the adjustments were made.

在我们的分析中，我们发现调整后均值误差显著降低。

2.A lower mean error suggests that the model is better at making predictions.

较低的均值误差表明该模型在预测方面表现更好。

3.To improve accuracy, we need to minimize the mean error in our predictions.

为了提高准确性，我们需要最小化预测中的均值误差。

4.The model's performance was evaluated using the mean error, which indicates the average deviation from the actual values.

该模型的性能通过均值误差进行评估，表示与实际值的平均偏差。

5.The mean error of the forecast was calculated to assess its reliability.

为了评估预测的可靠性，计算了均值误差。

作文

In the world 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 evaluating the performance of predictive models. The mean error is defined as the average of the errors between the predicted values and the actual values. It provides insight into how far off predictions are from reality, thus helping analysts and researchers make informed decisions based on their data.To comprehend the concept of mean error, let’s consider a simple example. Imagine a scenario where a weather forecasting model predicts the temperature for a week. If the actual temperatures for those days are recorded, we can calculate the mean error by taking the difference between the predicted temperatures and the actual temperatures for each day. By summing these differences and dividing by the number of observations, we arrive at the mean error. This metric can be both positive and negative, indicating whether the model tends to overestimate or underestimate the actual values. A positive mean error suggests that the predictions are generally lower than the actual values, while a negative mean error indicates the opposite. Therefore, a smaller absolute value of mean error signifies a better-performing model, as it means the predictions are closer to the actual values.However, it is essential to recognize that the mean error alone may not provide a complete picture of a model’s performance. For instance, if a model consistently underestimates a value but does so with a small margin, the mean error might still appear acceptable. This is where additional metrics, such as mean absolute error (MAE) or mean squared error (MSE), come into play. These metrics account for the magnitude of errors more effectively and can reveal patterns that the mean error might obscure.In practical applications, the mean error is widely used across various fields, including finance, engineering, and healthcare. For example, in finance, analysts utilize this metric to assess the accuracy of stock price predictions. In healthcare, predictive models can help estimate patient outcomes, and the mean error can indicate the reliability of these predictions. Understanding how to calculate and interpret the mean error is therefore vital for professionals working with data.In conclusion, the mean error serves as a foundational metric in the realm of data analysis. It offers valuable insights into the accuracy of predictive models and helps guide decision-making processes. While it is an essential tool, it should be used in conjunction with other metrics to ensure a comprehensive evaluation of a model's performance. As we continue to rely on data-driven approaches in various sectors, mastering the concept of mean error will undoubtedly enhance our ability to analyze and interpret complex datasets effectively.

在统计学和数据分析的世界中，理解各种指标对于准确解释结果至关重要。其中一个重要的指标是均值误差，它在评估预测模型性能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误差的平均值。它提供了对预测与现实之间差距的洞察，从而帮助分析师和研究人员根据数据做出明智的决策。为了理解均值误差的概念，让我们考虑一个简单的例子。想象一个天气预报模型预测一周的温度。如果记录下这些天的实际温度，我们可以通过计算每一天预测温度与实际温度之间的差异来计算均值误差。通过将这些差异相加并除以观察数量，我们得出了均值误差。这个指标可以是正值也可以是负值，表明模型是倾向于高估还是低估实际值。正的均值误差表明预测值通常低于实际值，而负的均值误差则表示相反。因此，较小的绝对值均值误差意味着模型表现更好，因为这意味着预测值更接近实际值。然而，必须认识到，仅仅依靠均值误差可能无法提供模型性能的完整图景。例如，如果一个模型持续低估某个值，但误差幅度很小，则均值误差可能仍然看起来可以接受。在这里，其他指标如均方根误差(MAE)或均方误差(MSE)就派上用场了。这些指标更有效地考虑了误差的大小，并可以揭示均值误差可能掩盖的模式。在实际应用中，均值误差被广泛用于各个领域，包括金融、工程和医疗保健。例如，在金融领域，分析师利用这一指标来评估股票价格预测的准确性。在医疗保健中，预测模型可以帮助估计患者的结果，而均值误差可以指示这些预测的可靠性。因此，了解如何计算和解释均值误差对于从事数据工作专业人士至关重要。总之，均值误差在数据分析领域中作为一个基础指标，提供了对预测模型准确性的宝贵洞察，并帮助指导决策过程。虽然它是一个重要工具，但应与其他指标结合使用，以确保对模型性能的全面评估。随着我们在各个行业越来越依赖数据驱动的方法，掌握均值误差的概念无疑将增强我们有效分析和解释复杂数据集的能力。