mean absolute error; absolute average error

简明释义

绝对平均误差

英英释义

例句

1.We found that the mean absolute error 平均绝对误差 was higher during the winter months.

我们发现冬季月份的平均绝对误差 mean absolute error 较高。

2.The mean absolute error 平均绝对误差 of our model was significantly lower than the previous version.

我们模型的平均绝对误差 mean absolute error 显著低于以前的版本。

3.In machine learning, minimizing the absolute average error 绝对平均误差 is crucial for improving model performance.

在机器学习中，最小化绝对平均误差 absolute average error 对于提高模型性能至关重要。

4.A low mean absolute error 平均绝对误差 indicates that our predictions are close to the actual values.

较低的平均绝对误差 mean absolute error 表明我们的预测接近实际值。

5.To evaluate the forecast accuracy, we calculated the absolute average error 绝对平均误差 for each prediction.

为了评估预测的准确性，我们计算了每个预测的绝对平均误差 absolute average error。

作文

In the field of data analysis and statistics, evaluating the accuracy of predictions and models is crucial. One of the most widely used metrics for assessing the performance of a model is the mean absolute error, which is often referred to as absolute average error. This metric provides a straightforward way to quantify how far off predictions are from actual values, making it easier for analysts to understand the effectiveness of their models.The mean absolute error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction. It is calculated by taking the absolute differences between predicted values and actual values, summing those differences, and then dividing by the number of observations. The formula can be expressed as follows:MAE = (1/n) * Σ |actual - predicted|where n is the number of observations, actual represents the true values, and predicted represents the values generated by the model. This calculation results in a non-negative value, with a lower MAE indicating a better fit of the model to the data.One of the main advantages of using mean absolute error is its interpretability. Unlike other error metrics that can be influenced heavily by outliers, such as the mean squared error (MSE), MAE treats all errors equally. This characteristic makes it particularly useful in scenarios where we want a robust measure of accuracy that is not skewed by extreme values. For instance, in financial forecasting, where outlier transactions can occur, MAE provides a clearer picture of overall prediction performance.Moreover, the absolute average error offers a simple yet effective way to communicate the average error in predictions to stakeholders who may not have a technical background. By presenting the error in a straightforward manner, businesses can make informed decisions based on the reliability of their predictive models. This is especially important in industries like healthcare, where accurate predictions can significantly impact patient outcomes.However, while mean absolute error is a valuable metric, it is essential to consider it alongside other evaluation metrics. For example, in cases where the scale of errors varies significantly, combining MAE with metrics like MSE or R-squared can provide a more comprehensive view of model performance. Furthermore, it is vital to understand the context of the data being analyzed; different fields may prioritize different aspects of accuracy depending on their specific needs.In conclusion, the mean absolute error, or absolute average error, serves as an essential tool in the realm of data analytics. Its ability to provide a clear and interpretable measure of prediction accuracy makes it a favorite among data scientists and analysts. While it is important to use this metric judiciously and in conjunction with other evaluation methods, its significance cannot be overstated. As data continues to play an increasingly pivotal role in decision-making across various sectors, mastering the use of MAE will undoubtedly enhance the quality and reliability of predictive modeling efforts.

在数据分析和统计学领域，评估预测和模型的准确性至关重要。评估模型性能的最广泛使用的指标之一是均值绝对误差，通常被称为绝对平均误差。该指标提供了一种简单的方法来量化预测与实际值之间的偏差，使分析师更容易理解其模型的有效性。均值绝对误差（MAE）测量一组预测中的错误平均幅度，而不考虑其方向。它通过计算预测值和实际值之间的绝对差异，将这些差异相加，然后除以观察值的数量来进行计算。公式可以表示如下：MAE = (1/n) * Σ |实际 - 预测|其中n是观察值的数量，实际代表真实值，预测代表模型生成的值。此计算结果为非负值，较低的MAE表示模型与数据的拟合效果更好。使用均值绝对误差的主要优点之一是其可解释性。与其他可能受到极端值严重影响的误差指标（如均方误差（MSE））不同，MAE平等对待所有错误。这一特性使其在我们希望获得不受极端值影响的鲁棒准确性度量的场景中特别有用。例如，在金融预测中，极端交易可能会发生，MAE提供了整体预测性能的更清晰图景。此外，绝对平均误差提供了一种简单而有效的方法，将预测中的平均误差传达给可能没有技术背景的利益相关者。通过以直接的方式呈现误差，企业能够基于其预测模型的可靠性做出明智的决策。这在医疗保健等行业尤为重要，因为准确的预测可能会显著影响患者的结果。然而，尽管均值绝对误差是一个有价值的指标，但考虑它与其他评估指标一起使用是至关重要的。例如，在错误规模变化显著的情况下，将MAE与MSE或R平方等指标结合使用，可以提供更全面的模型性能视图。此外，了解所分析数据的背景也至关重要；不同领域可能根据其特定需求优先考虑准确性的不同方面。总之，均值绝对误差或绝对平均误差在数据分析领域中作为一种重要工具。它提供了预测准确性的清晰和可解释的度量，使其成为数据科学家和分析师的最爱。虽然重要的是谨慎使用这一指标，并与其他评估方法结合使用，但其重要性不容小觑。随着数据在各个行业决策中扮演越来越重要的角色，掌握MAE的使用无疑将提升预测建模工作的质量和可靠性。