balanced error

简明释义

1. 对称误差; 2. 差额;

英英释义

例句

1.We need to minimize the balanced error 平衡误差 to improve our predictive accuracy.

我们需要最小化平衡误差 平衡误差 以提高我们的预测准确性。

2.In machine learning, achieving a low balanced error 平衡误差 is crucial for model performance.

在机器学习中，达到低的平衡误差 平衡误差 对于模型性能至关重要。

3.A balanced error 平衡误差 indicates that the model performs equally well on both classes.

一个平衡误差 平衡误差 表明模型在两个类别上表现同样良好。

4.The researchers presented their findings on balanced error 平衡误差 during the conference.

研究人员在会议上展示了他们关于平衡误差 平衡误差 的发现。

5.The team analyzed the balanced error 平衡误差 rates across different algorithms to find the best fit.

团队分析了不同算法的平衡误差 平衡误差 率，以找到最佳匹配。

作文

In the world of data science and machine learning, one of the critical concepts that practitioners must understand is the idea of a balanced error. This term refers to a situation where the errors made by a predictive model are distributed evenly across different classes or categories. In simpler terms, it means that the model does not favor one class over another when making predictions. Achieving balanced error is essential for creating reliable models, especially in scenarios where the cost of misclassification varies significantly between classes.For instance, consider a medical diagnosis model that predicts whether a patient has a certain disease. If the model consistently predicts 'no disease' for patients who actually have it, it may lead to severe consequences, including the patient's health deteriorating due to lack of treatment. In this case, a balanced error would mean that the model correctly identifies both positive and negative cases with equal accuracy, thus minimizing the risk of harmful outcomes.Another example can be seen in fraud detection systems. These systems must identify fraudulent transactions without flagging too many legitimate ones. A model that achieves balanced error will ensure that it catches most fraudulent activities while maintaining a low false-positive rate. This balance is crucial for maintaining customer trust and satisfaction, as excessive false alarms can frustrate users and lead to a poor user experience.To achieve a balanced error, data scientists often use various techniques. One common approach is to ensure that the training dataset is representative of all classes. If one class is underrepresented, the model might learn to ignore it, leading to biased predictions. Techniques such as oversampling the minority class or undersampling the majority class can help create a more balanced dataset.Moreover, evaluation metrics play a vital role in understanding balanced error. Traditional accuracy measures may not be sufficient if one class dominates the dataset. Instead, metrics like precision, recall, and the F1 score provide a more nuanced view of model performance. By focusing on these metrics, data scientists can better assess whether their models are achieving a balanced error and make necessary adjustments.In conclusion, the concept of balanced error is paramount in the field of predictive modeling. It emphasizes the importance of fairness and equity in model predictions, ensuring that no class is unfairly treated. As we continue to develop more sophisticated algorithms and models, striving for a balanced error will remain a fundamental goal. This pursuit not only enhances the reliability of our models but also safeguards against potential risks associated with biased predictions. The implications of achieving balanced error extend beyond technical performance; they touch upon ethical considerations and the responsibility of data scientists to create fair and just systems for all users.

在数据科学和机器学习的世界中，实践者必须理解的一个关键概念是“平衡误差”的概念。这个术语指的是预测模型所犯的错误在不同类别或类之间均匀分布的情况。简单来说，这意味着模型在做出预测时不会偏向某一类。在创建可靠模型时，实现平衡误差至关重要，尤其是在误分类的成本在不同类别之间显著变化的情况下。例如，考虑一个医疗诊断模型，该模型预测患者是否患有某种疾病。如果模型始终预测患者没有疾病，而实际上他们确实患有疾病，这可能会导致严重后果，包括患者因缺乏治疗而健康恶化。在这种情况下，平衡误差意味着模型能够以相等的准确性正确识别阳性和阴性病例，从而最大限度地降低危害结果的风险。另一个例子可以在欺诈检测系统中看到。这些系统必须识别欺诈性交易，同时不标记过多的合法交易。实现平衡误差的模型将确保它能够抓住大多数欺诈活动，同时保持较低的误报率。这种平衡对于维护客户信任和满意度至关重要，因为过多的误报会使用户感到沮丧，并导致糟糕的用户体验。为了实现平衡误差，数据科学家通常使用各种技术。一种常见的方法是确保训练数据集代表所有类别。如果某一类别的样本不足，模型可能会学会忽略它，从而导致偏见的预测。技术如对少数类进行过采样或对多数类进行欠采样可以帮助创建更平衡的数据集。此外，评估指标在理解平衡误差中发挥着至关重要的作用。传统的准确率指标可能不足以反映一个类别主导数据集的情况。相反，像精确率、召回率和F1分数这样的指标提供了对模型性能更细致的视角。通过关注这些指标，数据科学家可以更好地评估他们的模型是否实现了平衡误差，并做出必要的调整。总之，平衡误差的概念在预测建模领域至关重要。它强调了模型预测中的公平性和公正性的重要性，确保没有类别受到不公平的对待。随着我们继续开发更复杂的算法和模型，追求平衡误差将始终是一个基本目标。这一追求不仅提高了我们模型的可靠性，还保护了我们免受与偏见预测相关的潜在风险。实现平衡误差的影响超越了技术性能；它涉及伦理考虑，以及数据科学家为所有用户创建公平和公正系统的责任。