imbalanced entry

简明释义

不平衡分录

英英释义

例句

1.The software flagged an imbalanced entry in the transaction log, which required further investigation.

软件在交易日志中标记了一个不平衡的条目，需要进一步调查。

2.To resolve the issue, we need to correct the imbalanced entry in the financial statements.

为了解决这个问题，我们需要更正财务报表中的不平衡的条目。

3.During the audit, we discovered an imbalanced entry that could lead to significant financial discrepancies.

在审计过程中，我们发现一个不平衡的条目，这可能导致重大财务差异。

4.The system reported an imbalanced entry in the database, indicating a mismatch in the data records.

系统报告在数据库中存在一个不平衡的条目，这表明数据记录存在不匹配。

5.An imbalanced entry can result in incorrect reporting and analysis of the data.

不平衡的条目可能导致数据的错误报告和分析。

作文

In the world of data analysis and statistics, one often encounters various challenges that can lead to misleading conclusions. One such challenge is the phenomenon known as imbalanced entry, which refers to a situation where the data collected for analysis is not evenly distributed across different categories or classes. This can significantly impact the results of any analysis and lead to biased interpretations. For instance, in a study examining the effectiveness of a new drug, if the sample size consists of predominantly male participants while only a few females are included, the findings may not accurately represent the drug's effects on the entire population. This is a clear example of imbalanced entry, where the lack of balance in the data set can skew results and affect the overall validity of the research. When data is imbalanced, it can lead to several issues in statistical modeling and machine learning. Models trained on such data may become biased towards the majority class, resulting in poor performance when predicting outcomes for the minority class. For example, in fraud detection systems, if the training data contains 95% legitimate transactions and only 5% fraudulent ones, the model may learn to classify most transactions as legitimate, failing to identify actual fraud cases effectively. This scenario highlights the importance of addressing imbalanced entry to ensure that models are robust and reliable.To mitigate the effects of imbalanced entry, researchers and data scientists can employ various techniques. One common approach is resampling the dataset to create a more balanced distribution. This can be done through oversampling the minority class or undersampling the majority class. Another method is to use algorithms specifically designed to handle imbalanced data, such as cost-sensitive learning, where different misclassification costs are assigned to different classes. These strategies can help improve model performance and provide a more accurate representation of the underlying data.Moreover, it is essential to evaluate models using appropriate metrics that account for class imbalance. Traditional accuracy measures may be misleading in the presence of imbalanced entry. Instead, metrics such as precision, recall, and the F1 score can provide a better understanding of how well the model performs, especially for the minority class. By focusing on these metrics, researchers can ensure that their models are not only accurate but also fair and effective in real-world applications.In conclusion, imbalanced entry poses significant challenges in data analysis, leading to biased results and unreliable models. It is crucial for researchers and practitioners to recognize and address this issue through various techniques and appropriate evaluation metrics. By doing so, they can enhance the validity of their findings and contribute to more equitable outcomes in fields such as healthcare, finance, and beyond. Understanding and tackling imbalanced entry is vital for achieving accurate and meaningful insights from data, ultimately benefiting society as a whole.

在数据分析和统计的世界中，人们常常会遇到各种挑战，这些挑战可能导致误导性的结论。其中一个挑战是被称为不平衡输入的现象，它指的是在分析中收集的数据在不同类别或类之间分布不均。这可能会显著影响任何分析的结果，并导致偏见的解释。例如，在一项研究中，如果样本量主要由男性参与者组成，而只有少数女性参与，那么研究结果可能无法准确代表该药物对整个群体的影响。这是不平衡输入的一个明确示例，其中数据集的不平衡性可能导致结果偏差并影响研究的整体有效性。当数据不平衡时，可能会在统计建模和机器学习中导致几个问题。在这样的数据上训练的模型可能会对多数类产生偏见，导致在预测少数类的结果时表现不佳。例如，在欺诈检测系统中，如果训练数据包含95%的合法交易和仅5%的欺诈交易，则模型可能会学习将大多数交易分类为合法，从而未能有效识别实际的欺诈案例。这种情况突显了处理不平衡输入的重要性，以确保模型的稳健性和可靠性。为了减轻不平衡输入的影响，研究人员和数据科学家可以采用各种技术。一种常见的方法是对数据集进行重采样，以创建更平衡的分布。这可以通过对少数类进行过采样或对多数类进行欠采样来完成。另一种方法是使用专门设计用于处理不平衡数据的算法，例如成本敏感学习，其中为不同类分配不同的错误分类成本。这些策略可以帮助提高模型性能，并提供对潜在数据的更准确表示。此外，使用适当的评估指标来评估模型也至关重要，这些指标考虑了类的不平衡性。在存在不平衡输入的情况下，传统的准确性度量可能具有误导性。相反，精确度、召回率和F1分数等指标可以更好地理解模型的表现，特别是对于少数类。通过关注这些指标，研究人员可以确保他们的模型不仅准确，而且在现实应用中公平有效。总之，不平衡输入在数据分析中带来了重大挑战，导致偏见结果和不可靠模型。研究人员和从业者必须认识到并解决这一问题，通过各种技术和适当的评估指标。通过这样做，他们可以增强其发现的有效性，并为医疗保健、金融等领域的更公平结果做出贡献。理解和应对不平衡输入对于从数据中获得准确和有意义的洞察至关重要，最终使整个社会受益。