absolute value error

简明释义

绝对误差;

英英释义

例句

1.When analyzing data, we often calculate the absolute value error to evaluate performance.

在分析数据时，我们经常计算绝对值误差来评估性能。

2.In machine learning, minimizing the absolute value error can lead to more accurate models.

在机器学习中，最小化绝对值误差可以导致更精确的模型。

3.A lower absolute value error suggests a better fit for the model.

较低的绝对值误差表明模型的拟合效果更好。

4.The absolute value error indicates how far off our prediction is from the actual value.

该绝对值误差表示我们的预测与实际值之间的偏差有多大。

5.The absolute value error helps us understand the accuracy of our calculations.

该绝对值误差帮助我们理解计算的准确性。

作文

In mathematical analysis and statistics, understanding the concept of error is crucial for evaluating the accuracy of predictions and measurements. One specific type of error that plays a significant role in various fields, including engineering and data science, is the absolute value error. The absolute value error refers to the absolute difference between a measured or estimated value and the actual value. This metric is essential because it provides a clear indication of how far off a prediction or measurement is from the true value, irrespective of the direction of the error. For instance, imagine a scenario where an engineer is designing a bridge and needs to calculate the load it can bear. If the actual load capacity is 100 tons, but the engineer estimates it to be 90 tons, the absolute value error would be |100 - 90| = 10 tons. This means the estimate is off by 10 tons, which is critical information for ensuring safety and reliability in engineering projects. The importance of the absolute value error extends beyond engineering. In the realm of machine learning, this metric is often used to assess the performance of predictive models. For example, when a model predicts housing prices, the absolute value error can help analysts understand how close their predictions are to the actual sale prices of houses. By calculating the absolute value error for multiple predictions, analysts can derive insights into the model’s accuracy and make necessary adjustments to improve its performance. Moreover, the absolute value error is particularly useful because it treats all errors equally, regardless of whether they are positive or negative. This characteristic makes it a preferred choice in many applications where the direction of the error is not as important as the magnitude. For example, in financial forecasting, a company may want to know how far off its revenue projections are from actual results. Here, the absolute value error gives a straightforward measure of deviation without getting bogged down by whether the forecasts were over or under the actual figures. However, while the absolute value error is a valuable tool, it has its limitations. One major drawback is that it does not provide information about the direction of the error. This limitation can sometimes be crucial in contexts where understanding whether a value is overestimated or underestimated matters significantly. For instance, in quality control processes, knowing whether a product's measurement is too high or too low can influence decisions on adjustments and corrections. To address this, analysts often look at other metrics alongside the absolute value error, such as the mean squared error (MSE) or the root mean squared error (RMSE), which take into account the direction of the error. These additional metrics can provide a more comprehensive view of model performance and measurement accuracy. In conclusion, the absolute value error serves as a fundamental concept in various disciplines, providing a simple yet effective way to quantify the accuracy of predictions and measurements. Its ability to convey the magnitude of error without regard to direction makes it a widely used metric in engineering, data science, and financial analysis. However, it is essential to complement it with other measures to obtain a fuller picture of accuracy and performance. Understanding the absolute value error and its implications can enhance decision-making processes and lead to better outcomes in both theoretical and practical applications.

在数学分析和统计学中，理解误差的概念对于评估预测和测量的准确性至关重要。一种在工程和数据科学等多个领域发挥着重要作用的特定类型的误差是绝对值误差。绝对值误差是指测量值或估计值与实际值之间的绝对差。这个指标很重要，因为它清晰地表明预测或测量与真实值的偏差程度，而不考虑误差的方向。例如，想象一个工程师正在设计一座桥，并需要计算其承载能力。如果实际承载能力为100吨，但工程师估计为90吨，则绝对值误差为|100 - 90| = 10吨。这意味着估计值偏离了10吨，这对于确保工程项目的安全性和可靠性至关重要。绝对值误差的重要性不仅限于工程。在机器学习领域，这个指标通常用于评估预测模型的性能。例如，当一个模型预测房价时，绝对值误差可以帮助分析师了解他们的预测与实际房屋售价的接近程度。通过计算多个预测的绝对值误差，分析师可以获得关于模型准确性的见解，并进行必要的调整以提高其性能。此外，绝对值误差特别有用，因为它平等对待所有误差，无论是正误差还是负误差。这一特性使其成为许多应用的首选，尤其是在误差的方向并不如大小重要的情况下。例如，在财务预测中，一家公司可能想知道其收入预测与实际结果之间的偏差有多大。在这里，绝对值误差提供了一种简单的偏差测量，而不必纠结于预测是高估还是低估了实际数字。然而，尽管绝对值误差是一个有价值的工具，但它也有其局限性。一个主要的缺点是它不提供有关误差方向的信息。这一限制在某些上下文中可能至关重要，例如在质量控制过程中，了解产品的测量值是偏高还是偏低可能会影响调整和修正的决策。为了应对这一问题，分析师通常会同时考虑其他指标，如均方误差（MSE）或均方根误差（RMSE），这些指标考虑了误差的方向。这些附加指标可以提供更全面的模型性能和测量准确性的视图。总之，绝对值误差在多个学科中作为一个基本概念，提供了一种简单而有效的方法来量化预测和测量的准确性。它在不考虑方向的情况下传达误差的大小，使其成为工程、数据科学和财务分析中广泛使用的指标。然而，必须与其他测量相结合，以获得更全面的准确性和性能图景。理解绝对值误差及其影响可以增强决策过程，带来更好的理论和实际应用效果。