mean absolute error

简明释义

平均绝对误差

英英释义

例句

1.We calculated the mean absolute error 平均绝对误差 to determine how close our predictions were to the actual values.

我们计算了平均绝对误差，以确定我们的预测与实际值的接近程度。

2.In our analysis, the mean absolute error 平均绝对误差 was found to be significantly lower than previous studies.

在我们的分析中，平均绝对误差显著低于以往的研究。

3.The mean absolute error 平均绝对误差 is commonly used to evaluate the performance of regression models.

平均绝对误差通常用于评估回归模型的性能。

4.To improve our model, we focused on reducing the mean absolute error 平均绝对误差 during training.

为了改善我们的模型，我们在训练过程中专注于减少平均绝对误差。

5.A lower mean absolute error 平均绝对误差 indicates a better fitting model.

较低的平均绝对误差表明模型拟合得更好。

作文

In the field of statistics and data analysis, accuracy is paramount. One of the most widely used metrics to evaluate the accuracy of a predictive model is the mean absolute error. The mean absolute error (MAE) measures the average magnitude of the errors in a set of predictions, without considering their direction. It is calculated as the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight. This means that it gives a linear score which does not square the errors, making it less sensitive to outliers compared to other metrics like mean squared error (MSE). Understanding the mean absolute error is crucial for anyone involved in data science or machine learning. For instance, if a company is trying to predict sales for the next quarter based on historical data, they would want to measure how accurate their predictions are. By calculating the mean absolute error, they can determine how far off their predictions were from the actual sales figures. A lower MAE indicates a more accurate model, while a higher MAE suggests the need for improvement in the model's predictive capabilities.The formula for calculating the mean absolute error is straightforward: you take the absolute value of each error (the difference between predicted and actual values), sum these absolute values, and then divide by the number of observations. Mathematically, it can be expressed as:MAE = (1/n) * Σ |yi - xi|,where yi is the predicted value, xi is the actual value, and n is the number of observations. This simplicity is one of the reasons why the mean absolute error is favored by many analysts.Moreover, the mean absolute error has practical applications beyond just sales forecasting. It can be used in various domains such as weather forecasting, stock market predictions, and even in evaluating the performance of machine learning algorithms. For example, if a weather model predicts temperatures for the week ahead, the mean absolute error can help meteorologists understand how close their predictions were to the actual recorded temperatures. This feedback is essential for refining models and improving future predictions.One of the benefits of using the mean absolute error is that it is easy to interpret. Unlike squared errors, which can exaggerate the impact of larger errors, the MAE provides a clear representation of average error magnitude. This makes it user-friendly, especially for stakeholders who may not have a technical background. They can easily grasp what an MAE of, say, 5 units means in the context of their data.However, it is important to note that the mean absolute error is not without its limitations. It treats all errors equally, which means that it does not differentiate between overestimations and underestimations. In some cases, this might not provide a complete picture of a model's performance. Therefore, it is often recommended to use MAE in conjunction with other metrics, such as MSE or root mean square error (RMSE), to get a comprehensive understanding of a model’s accuracy.In conclusion, the mean absolute error is a fundamental concept in the realm of statistics and predictive modeling. Its ease of calculation and interpretation makes it a popular choice among analysts and data scientists alike. By leveraging the insights gained from MAE, organizations can make informed decisions, refine their predictive models, and ultimately improve their operational efficiencies. As industries continue to rely on data-driven decision-making, mastering the mean absolute error will be an invaluable skill for professionals in the field.

在统计学和数据分析领域，准确性至关重要。评估预测模型准确性的最广泛使用的指标之一是平均绝对误差。平均绝对误差（MAE）衡量一组预测中错误的平均大小，而不考虑它们的方向。它的计算方法是对测试样本中的绝对差值进行平均，其中所有单个差值具有相等的权重。这意味着它提供一个线性分数，不对错误进行平方处理，因此相比于其他指标，如均方误差（MSE），对异常值的敏感性较低。理解平均绝对误差对于任何参与数据科学或机器学习的人来说都是至关重要的。例如，如果一家公司试图根据历史数据预测下一个季度的销售额，他们会想要衡量其预测的准确性。通过计算平均绝对误差，他们可以确定其预测与实际销售数字之间的偏差程度。较低的MAE表示模型更准确，而较高的MAE则表明模型的预测能力需要改进。计算平均绝对误差的公式很简单：你需要取每个错误的绝对值（预测值与实际值之间的差异），将这些绝对值相加，然后除以观察次数。在数学上，它可以表示为：MAE = (1/n) * Σ |yi - xi|，其中yi是预测值，xi是实际值，n是观察次数。这种简单性是许多分析师偏爱平均绝对误差的原因之一。此外，平均绝对误差的实际应用超出了销售预测的范围。它可以用于各种领域，例如天气预报、股市预测，甚至评估机器学习算法的性能。例如，如果一个天气模型预测未来一周的气温，平均绝对误差可以帮助气象学家了解他们的预测与实际记录的气温之间的接近程度。这种反馈对于完善模型和改善未来的预测至关重要。使用平均绝对误差的好处之一是它易于解释。与平方误差不同，后者可能夸大较大错误的影响，MAE提供了平均误差大小的清晰表示。这使得它对利益相关者尤其友好，尤其是那些可能没有技术背景的人。他们可以轻松理解，比如说，MAE为5单位在其数据上下文中意味着什么。然而，需要注意的是，平均绝对误差并非没有局限性。它对所有错误的处理是一样的，这意味着它不会区分高估和低估。在某些情况下，这可能无法提供模型性能的完整图景。因此，通常建议将MAE与其他指标结合使用，如MSE或均方根误差（RMSE），以全面了解模型的准确性。总之，平均绝对误差是统计学和预测建模领域的基本概念。它的计算和解释的简便性使其成为分析师和数据科学家普遍选择的工具。通过利用从MAE中获得的见解，组织可以做出明智的决策，完善其预测模型，最终提高运营效率。随着各行业继续依赖数据驱动的决策，掌握平均绝对误差将成为该领域专业人士的一项宝贵技能。