mean error

简明释义

平均误差

英英释义

例句

1.The mean error of the sensor readings was calculated to assess its reliability.

传感器读数的均值误差被计算出来以评估其可靠性。

2.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。

3.After adjusting the parameters, the mean error decreased significantly.

调整参数后，均值误差显著降低。

4.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。

5.In statistical analysis, the mean error helps us understand how close our estimates are to the true values.

在统计分析中，均值误差帮助我们理解我们的估计值与真实值的接近程度。

作文

In the realm of data analysis and statistics, understanding various metrics is crucial for interpreting results accurately. One such metric that often comes up is the mean error. The term mean error refers to the average of the differences between predicted values and actual values in a dataset. This concept is fundamental in fields such as machine learning, economics, and engineering, where accurate predictions are essential for making informed decisions. To illustrate the significance of mean error, let us consider a simple example. Suppose a company forecasts its sales for the upcoming quarter based on historical data. If the actual sales figures deviate from these predictions, the company needs to understand the extent of this deviation. By calculating the mean error, the company can quantify how far off their predictions were on average. This information can then be used to adjust future forecasts and improve the accuracy of their predictive models.The calculation of mean error is straightforward. It involves taking the sum of all the errors (the differences between predicted and actual values) and dividing it by the number of observations. Mathematically, it can be expressed as:Mean Error = (Σ (Predicted Value - Actual Value)) / Nwhere N is the total number of observations. A positive mean error indicates that the predictions tend to underestimate the actual values, while a negative mean error suggests an overestimation. Ideally, a mean error close to zero signifies that the model's predictions are generally accurate, but it is important to note that this metric alone does not provide a complete picture of prediction accuracy.Another important aspect to consider is that the mean error can sometimes be misleading. For instance, if there are outliers in the data, they can disproportionately affect the mean error, leading to potentially erroneous conclusions. Therefore, it is often beneficial to complement the mean error with other metrics, such as the mean absolute error (MAE) or root mean square error (RMSE), which provide additional insights into the performance of predictive models.In practical applications, businesses and researchers regularly use mean error to evaluate the performance of their models. For example, in finance, analysts may use mean error to assess the accuracy of stock price predictions. In healthcare, it can help in evaluating the effectiveness of treatment plans by comparing predicted outcomes to actual patient results. Understanding the mean error and its implications allows professionals to refine their approaches and make better decisions based on data. As we become increasingly reliant on data-driven insights, mastering metrics like mean error will be vital for success across various industries. In conclusion, the mean error serves as a critical tool for assessing the accuracy of predictions in numerous fields. While it provides valuable information regarding the average deviation of predictions from actual outcomes, it is essential to consider it alongside other metrics to gain a comprehensive understanding of a model's performance. By doing so, organizations can enhance their predictive capabilities and ultimately achieve better results in their respective domains.

在数据分析和统计学领域，理解各种指标对于准确解读结果至关重要。其中一个常见的指标是均值误差。术语均值误差指的是数据集中预测值与实际值之间差异的平均值。这个概念在机器学习、经济学和工程等领域尤为重要，因为准确的预测对于做出明智的决策至关重要。为了说明均值误差的重要性，我们来考虑一个简单的例子。假设一家公司根据历史数据预测下个季度的销售额。如果实际销售数字偏离了这些预测，公司需要了解这种偏差的程度。通过计算均值误差，公司可以量化他们的预测平均上有多远的偏差。然后，这些信息可以用来调整未来的预测，提高预测模型的准确性。均值误差的计算非常简单。它涉及到将所有误差（预测值与实际值之间的差异）的总和除以观察数。在数学上，可以表示为：均值误差 = (Σ (预测值 - 实际值)) / N其中N是观察的总数。正的均值误差表明预测通常低估了实际值，而负的均值误差则表明过高估计。理想情况下，接近零的均值误差表示模型的预测通常是准确的，但仅凭这个指标并不能提供完整的预测准确性图景。另一个需要考虑的重要方面是，均值误差有时可能会产生误导。例如，如果数据中存在异常值，它们可能会不成比例地影响均值误差，导致潜在的错误结论。因此，通常有利于将均值误差与其他指标（如平均绝对误差（MAE）或均方根误差（RMSE））结合使用，以提供关于预测模型性能的更多见解。在实际应用中，企业和研究人员定期使用均值误差来评估其模型的性能。例如，在金融领域，分析师可能会使用均值误差来评估股票价格预测的准确性。在医疗保健中，它可以通过比较预测结果与实际患者结果来帮助评估治疗计划的有效性。理解均值误差及其影响使专业人士能够完善他们的方法，并基于数据做出更好的决策。随着我们越来越依赖数据驱动的洞察，掌握像均值误差这样的指标对于各个行业的成功至关重要。总之，均值误差作为评估多个领域预测准确性的关键工具。虽然它提供了有关预测与实际结果之间平均偏差的有价值信息，但考虑到其他指标以全面理解模型的性能是至关重要的。通过这样做，组织可以增强其预测能力，并最终在各自领域取得更好的成果。