mean error

简明释义

平均误差

英英释义

例句

1.In statistical analysis, the mean error can indicate how well a model fits the data.

在统计分析中，均值误差可以指示模型与数据的拟合程度。

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

为了提高准确性，我们需要最小化我们预测中的均值误差。

3.The mean error of the forecast was significantly lower than previous attempts.

这次预测的均值误差显著低于之前的尝试。

4.Calculating the mean error helps identify systematic biases in the measurements.

计算均值误差有助于识别测量中的系统性偏差。

5.The model's performance was evaluated using the mean error, which is a measure of the average deviation from the actual values.

该模型的性能通过使用均值误差进行评估，这是对实际值平均偏差的衡量。

作文

In the field of statistics and data analysis, understanding the concept of mean error is crucial for evaluating the accuracy of predictions and measurements. The term mean error refers to the average of the differences between predicted values and actual values. This metric provides insight into how far off our predictions are from reality, which is essential in various applications, including machine learning, economics, and engineering. To illustrate the importance of mean error, consider a scenario where a company is trying to forecast its sales for the next quarter. If the sales team uses a predictive model to estimate sales based on historical data, the mean error will help them understand how reliable their predictions are. By calculating the mean error, they can identify whether their model consistently overestimates or underestimates sales figures. For instance, let's say the predicted sales for the last quarter were $100,000, but the actual sales turned out to be $90,000. If this pattern continues over several quarters, the company might find that their mean error is negative, indicating that their forecasts are generally too high. Conversely, if the actual sales were consistently higher than predicted, the mean error would be positive, suggesting that the sales team needs to adjust their forecasting model. Moreover, the mean error can also be beneficial when assessing the performance of different models. By comparing the mean error of multiple predictive models, analysts can determine which model performs best at making accurate predictions. A lower mean error signifies a more accurate model, guiding decision-makers towards the most effective strategies. It is important to note that while mean error provides valuable information, it does have limitations. For instance, the mean error does not indicate the direction of the error; it merely provides an average. Therefore, it is often used alongside other metrics, such as mean absolute error (MAE) or root mean square error (RMSE), which offer additional insights into prediction accuracy and the variability of errors. In conclusion, the concept of mean error is fundamental in the realm of data analysis and predictive modeling. It serves as a key indicator of the accuracy of predictions, allowing businesses and researchers to refine their models and improve decision-making processes. Understanding and effectively utilizing mean error can lead to better outcomes in various fields, ultimately enhancing the reliability of forecasts and analyses. As industries increasingly rely on data-driven decisions, mastering the use of mean error becomes ever more critical in ensuring success and accuracy in predictions.

在统计学和数据分析领域，理解均值误差的概念对于评估预测和测量的准确性至关重要。术语均值误差指的是预测值与实际值之间差异的平均值。这个指标提供了我们预测与现实相距多远的洞察，这在机器学习、经济学和工程等各种应用中都是必不可少的。为了说明均值误差的重要性，考虑一个场景：一家公司试图预测下一个季度的销售额。如果销售团队使用预测模型根据历史数据来估算销售额，均值误差将帮助他们了解预测的可靠性。通过计算均值误差，他们可以识别出模型是否持续高估或低估销售数字。例如，假设上一季度的预测销售额为100,000美元，但实际销售额为90,000美元。如果这种模式在几个季度内持续存在，公司可能会发现他们的均值误差为负，表明他们的预测通常偏高。相反，如果实际销售额持续高于预测，则均值误差将为正，表明销售团队需要调整他们的预测模型。此外，均值误差在评估不同模型的性能时也非常有用。通过比较多个预测模型的均值误差，分析师可以确定哪个模型在做出准确预测方面表现最佳。较低的均值误差意味着模型更准确，从而指导决策者朝着最有效的策略前进。需要注意的是，尽管均值误差提供了有价值的信息，但它也有其局限性。例如，均值误差并未指示误差的方向；它只是提供一个平均值。因此，它通常与其他指标一起使用，如平均绝对误差（MAE）或均方根误差（RMSE），这些指标提供了关于预测准确性和误差变异性的额外洞察。总之，均值误差的概念在数据分析和预测建模领域是基础性的。它作为预测准确性的关键指标，使企业和研究人员能够完善他们的模型并改善决策过程。理解和有效利用均值误差可以在各个领域带来更好的结果，最终提高预测和分析的可靠性。随着各行业越来越依赖数据驱动的决策，掌握均值误差的使用变得愈发关键，以确保预测的成功和准确性。