average error

简明释义

平均误差

英英释义

例句

1.In machine learning, the average error 平均误差 is a key metric for assessing model performance.

在机器学习中，平均误差 average error是评估模型性能的关键指标。

2.The model's performance was evaluated by calculating the average error 平均误差 across all test samples.

该模型的性能通过计算所有测试样本的平均误差 average error来评估。

3.After several iterations, the average error 平均误差 decreased significantly.

经过几次迭代，平均误差 average error显著下降。

4.The average error 平均误差 of the survey results indicated a significant bias in the data collection process.

调查结果的平均误差 average error表明数据收集过程中存在显著偏差。

5.To improve accuracy, we need to minimize the average error 平均误差 in our predictions.

为了提高准确性，我们需要最小化预测中的平均误差 average error。

作文

In the field of data analysis and statistics, the term average error is frequently encountered. It refers to the average of the differences between predicted values and actual values in a dataset. Understanding average error is crucial for evaluating the accuracy of predictive models and algorithms. When we develop a model to predict outcomes based on historical data, it is essential to measure how well our predictions align with reality. This is where average error comes into play. For instance, consider a scenario where a weather forecasting model predicts temperatures for a week. If the actual temperatures differ from the predicted ones, we can calculate the average error to quantify this discrepancy. By summing up the absolute differences between the predicted and actual temperatures and then dividing by the number of predictions, we arrive at the average error. A lower average error indicates a more accurate model, while a higher average error suggests that the model may need refinement. Moreover, average error is not limited to weather forecasts; it applies to various fields such as finance, engineering, and machine learning. In finance, for example, investors use predictive models to forecast stock prices. The average error helps them assess the reliability of these models. If the average error is consistently low over time, investors may feel more confident in their decision-making based on these forecasts. In engineering, when designing systems or products, engineers often rely on simulations to predict performance. The average error between simulated results and real-world testing can provide insights into the accuracy of the design process. If the average error is significant, engineers may need to revisit their assumptions and models to improve accuracy. Furthermore, understanding average error allows researchers and analysts to communicate the reliability of their findings effectively. When presenting data, stating the average error alongside the results provides context. For example, if a study claims that a new drug reduces symptoms by 50%, mentioning the average error can help stakeholders understand the variability and uncertainty surrounding that claim. In conclusion, the concept of average error is fundamental in assessing the performance of predictive models across various disciplines. It serves as a vital metric for understanding the accuracy of predictions and refining methodologies. As data-driven decision-making becomes increasingly prevalent, mastering the interpretation and calculation of average error will empower professionals to make informed choices based on reliable data. By incorporating this understanding into our analytical toolkit, we can enhance the quality of our predictions and ultimately achieve better outcomes in our respective fields.

在数据分析和统计学领域，术语平均误差是经常遇到的。它指的是数据集中预测值与实际值之间差异的平均值。理解平均误差对于评估预测模型和算法的准确性至关重要。当我们开发一个基于历史数据的结果预测模型时，测量我们的预测与现实的对齐程度是必不可少的。这就是平均误差发挥作用的地方。例如，考虑一个天气预报模型预测一周的气温。如果实际气温与预测的气温不同，我们可以计算平均误差来量化这种差异。通过将预测和实际气温之间的绝对差异相加，然后除以预测数量，我们得出了平均误差。较低的平均误差表示模型更准确，而较高的平均误差则表明模型可能需要改进。此外，平均误差并不限于天气预报；它适用于金融、工程和机器学习等多个领域。例如，在金融领域，投资者使用预测模型来预测股票价格。平均误差帮助他们评估这些模型的可靠性。如果平均误差在一段时间内持续较低，投资者可能会对基于这些预测的决策感到更有信心。在工程领域，当设计系统或产品时，工程师通常依赖于模拟来预测性能。模拟结果与现实测试之间的平均误差可以提供有关设计过程准确性的见解。如果平均误差显著，工程师可能需要重新审视他们的假设和模型，以提高准确性。此外，理解平均误差使研究人员和分析师能够有效地传达其发现的可靠性。在呈现数据时，陈述平均误差与结果一起提供了背景。例如，如果一项研究声称一种新药能减少50%的症状，提及平均误差可以帮助利益相关者理解该声明的变异性和不确定性。总之，平均误差的概念在评估各学科预测模型的表现中是基础性的。它作为理解预测准确性和完善方法论的重要指标。随着数据驱动决策变得越来越普遍，掌握平均误差的解释和计算将使专业人员能够基于可靠数据做出明智的选择。通过将这种理解纳入我们的分析工具包，我们可以提高预测的质量，并最终在各自领域实现更好的结果。