back crossing

简明释义

回交

英英释义

例句

1.During the hike, we encountered a back crossing 回交 point that required careful navigation.

在徒步旅行中，我们遇到了一个需要小心导航的回交点。

2.In the software update process, a back crossing 回交 was necessary to revert to the previous version.

在软件更新过程中，进行一次回交是必要的，以恢复到之前的版本。

3.The team decided to perform a back crossing 回交 to ensure that all data was verified.

团队决定进行一次回交以确保所有数据都经过验证。

4.The pilot executed a back crossing 回交 maneuver to avoid turbulence.

飞行员执行了一次回交操作以避免湍流。

5.The river had a back crossing 回交 section that was particularly challenging for kayakers.

这条河有一个特别具有挑战性的回交段落，给皮划艇运动员带来了困难。

作文

In the world of mathematics, particularly in statistics and data analysis, the term back crossing refers to a specific method used to refine results or predictions by revisiting previous data points. This technique is essential in various fields, including machine learning, where models are often trained on historical data to make future predictions. The concept of back crossing can be understood as a way to ensure that the model does not overfit to the training data by allowing it to evaluate its predictions against past outcomes.When implementing back crossing, researchers typically divide their dataset into training and testing subsets. The training set is used to develop the predictive model, while the testing set serves as a benchmark to assess the model's performance. By applying back crossing, analysts can iteratively refine their models, using insights gained from previous iterations to improve accuracy and reliability.For instance, consider a scenario where a company wants to predict sales for the upcoming year based on historical data. Initially, they might create a model using data from the last five years. However, to enhance the model's predictive power, they might employ back crossing to analyze how well the model's predictions align with actual sales figures from previous years. This iterative process allows them to adjust their approach, ensuring that the model accounts for trends and anomalies that may have been overlooked in initial analyses.The importance of back crossing extends beyond mere accuracy in predictions. It fosters a deeper understanding of the underlying data patterns and relationships. By revisiting previous data points, analysts can identify potential biases or errors in their models, leading to more robust conclusions. In this sense, back crossing acts as a critical checkpoint in the analytical process, allowing for continuous improvement and adjustment.Moreover, back crossing is not limited to statistical models; it can also be applied in various other contexts. For example, in project management, teams may use a similar approach to review past project outcomes to inform future strategies. By reflecting on what worked and what didn’t, they can make informed decisions that enhance the likelihood of success in future endeavors.In summary, the concept of back crossing plays a vital role in refining predictive models and enhancing decision-making processes across various fields. Its iterative nature encourages continuous learning and adaptation, making it an invaluable tool for analysts and researchers alike. As we continue to navigate an increasingly data-driven world, the ability to effectively implement techniques like back crossing will be crucial for achieving accurate insights and making informed decisions. Therefore, understanding and mastering back crossing is essential for anyone looking to excel in data analysis and related disciplines.

在数学领域，特别是在统计和数据分析中，术语back crossing指的是一种特定的方法，用于通过重新审视先前的数据点来细化结果或预测。这种技术在多个领域中至关重要，包括机器学习，其中模型通常基于历史数据进行训练，以做出未来的预测。back crossing的概念可以理解为一种确保模型不对训练数据过拟合的方式，通过允许其根据过去的结果评估预测。在实施back crossing时，研究人员通常将他们的数据集划分为训练集和测试集。训练集用于开发预测模型，而测试集则作为评估模型性能的基准。通过应用back crossing，分析师可以迭代地细化他们的模型，利用从先前迭代中获得的见解来提高准确性和可靠性。例如，考虑一个公司希望根据历史数据预测未来一年的销售情况的场景。最初，他们可能会使用过去五年的数据创建一个模型。然而，为了增强模型的预测能力，他们可能会采用back crossing来分析模型的预测与过去几年实际销售数字之间的对齐程度。这一迭代过程使他们能够调整方法，确保模型考虑到可能在初步分析中被忽视的趋势和异常。back crossing的重要性不仅仅体现在预测的准确性上。它促进了对基础数据模式和关系的更深入理解。通过重新审视先前的数据点，分析师可以识别模型中的潜在偏差或错误，从而得出更稳健的结论。从这个意义上说，back crossing在分析过程中充当了一个关键的检查点，允许持续改进和调整。此外，back crossing并不限于统计模型；它也可以应用于其他各种上下文。例如，在项目管理中，团队可能会使用类似的方法来回顾过去项目的结果，以告知未来的策略。通过反思什么有效、什么无效，他们可以做出明智的决策，从而提高未来成功的可能性。总之，back crossing的概念在细化预测模型和增强各个领域的决策过程方面发挥着至关重要的作用。它的迭代特性鼓励持续学习和适应，使其成为分析师和研究人员不可或缺的工具。随着我们继续在一个日益数据驱动的世界中航行，有效实施像back crossing这样的技术的能力对于实现准确的洞察和做出明智的决策至关重要。因此，理解和掌握back crossing对于任何希望在数据分析及相关学科中脱颖而出的人来说都是必不可少的。