interpretable

简明释义

英[ɪnˈtɜːprətəbl]美[ɪnˈtɜːrprətəbl]

adj. 可说明的；可判断的；可翻译的

英英释义

单词用法

同义词

反义词

例句

1.Research on the interpretability has not got enough attention. This paper proposes a method of interpretable modeling based on fuzzy clustering in order to improve the interpretability of fuzzy model.

本文针对模糊建模过程中，系统的可解释性得不到保证的问题，提出一种基于模糊聚类的可解释性建模方法。

2.From the semantic point of view, parody in advertising idioms can be classified into the literally-interpretable phrases, the literally-uninterpretable phrases, and phrases of vague motivation.

从语义方面分析，换字成语可分为字面上通顺的、字面上解释不通的、似通非通的三类。

3.As test results are usually reported in the form of test scores, they must be interpretable, which involves the use of a certain scoring system.

由于考试结果通常用分数来表示，因此考试的分数必须具有可解释性。

4.After successful catheterization, optically interpretable images were obtained in 93.6%.

插管成功后，光解释图像中获得了93.6 %。

5.Occasionally, but not often, the newly created variables are interpretable.

偶尔，但不经常，新创建的变量被识别的。

6.Research on the interpretability has not got enough attention. This paper proposes a method of interpretable modeling based on fuzzy clustering in order to improve the interpretability of fuzzy model.

本文针对模糊建模过程中，系统的可解释性得不到保证的问题，提出一种基于模糊聚类的可解释性建模方法。

7.The financial report was interpretable even for those without a strong background in finance.

这份财务报告即使对于没有强大金融背景的人来说也很可解释。

8.The artist's work is interpretable in many ways, reflecting different emotions and thoughts.

这位艺术家的作品可以从多种角度进行解读，反映出不同的情感和思想。

9.In machine learning, models that are interpretable are often preferred for their transparency.

在机器学习中，可解释的模型通常因其透明性而受到青睐。

10.The results of the experiment were clear and interpretable, allowing the researchers to draw meaningful conclusions.

实验的结果清晰且可解释，使研究人员能够得出有意义的结论。

11.To ensure the model's effectiveness, it must be interpretable by its end-users.

为了确保模型的有效性，它必须对最终用户是可解释的。

作文

In today's world, data is generated at an unprecedented rate. From social media interactions to online transactions, the volume of information available is staggering. However, this abundance of data poses a significant challenge: how to make sense of it all. This is where the concept of being interpretable comes into play. In essence, an interpretable model is one that can be understood and explained in human terms, allowing users to grasp the reasoning behind its predictions and decisions.One of the primary reasons why interpretable models are essential is their role in fostering trust. Consider a medical diagnosis system powered by artificial intelligence. If a doctor relies on a model to suggest treatments, they need to understand why the model made a particular recommendation. If the model is not interpretable, the doctor may hesitate to follow its advice, fearing potential consequences for the patient. Conversely, if the model provides clear, understandable reasoning, the doctor can feel confident in its suggestions, leading to better patient outcomes.Moreover, interpretable models facilitate accountability. In sectors such as finance and criminal justice, decisions made by algorithms can have profound implications. If a loan application is denied or a person is flagged as a potential criminal, stakeholders must understand the rationale behind these decisions. Lack of transparency can lead to accusations of bias or discrimination. Therefore, ensuring that models are interpretable helps organizations stand accountable for their automated decisions, promoting fairness and equity.Furthermore, interpretable models allow for improved model performance. When data scientists can understand how a model arrives at its conclusions, they can identify areas for improvement. For instance, if a model consistently misclassifies certain data points, understanding its decision-making process can help researchers refine the model. This iterative process leads to more robust and effective algorithms, ultimately benefiting end-users.However, achieving interpretability is not without its challenges. In many cases, highly complex models, such as deep neural networks, offer remarkable predictive power but lack transparency. They operate as 'black boxes,' making it difficult to discern how inputs are transformed into outputs. As a result, researchers are actively exploring methods to enhance the interpretable nature of these models. Techniques such as feature importance analysis, local interpretable model-agnostic explanations (LIME), and SHAP (SHapley Additive exPlanations) are being developed to shed light on the inner workings of complex algorithms.In conclusion, as we navigate an increasingly data-driven landscape, the significance of interpretable models cannot be overstated. They empower users to trust automated systems, promote accountability, and enhance model performance. As technology continues to advance, prioritizing interpretability will be crucial in ensuring that data-driven decisions are fair, transparent, and beneficial for society as a whole. By striving for models that are interpretable, we can build a future where technology serves humanity with clarity and integrity.

在当今世界，数据以空前的速度生成。从社交媒体互动到在线交易，可用信息的数量令人震惊。然而，这种数据的丰富性带来了一个重大挑战：如何理解这一切。这就是可解释性的概念发挥作用的地方。实质上，可解释性模型是可以用人类术语理解和解释的模型，使用户能够掌握其预测和决策背后的推理。可解释性模型之所以至关重要，主要原因之一是它们在培养信任方面的作用。考虑一下由人工智能驱动的医学诊断系统。如果医生依赖某个模型来建议治疗，他们需要理解该模型为何做出特定的推荐。如果该模型不具备可解释性，医生可能会犹豫不决，不敢遵循其建议，担心对患者产生潜在后果。相反，如果模型提供清晰、易于理解的推理，医生就可以对其建议充满信心，从而改善患者的结果。此外，可解释性模型还促进了问责制。在金融和刑事司法等领域，算法做出的决定可能会产生深远的影响。如果贷款申请被拒绝或某人被标记为潜在罪犯，利益相关者必须理解这些决定背后的理由。缺乏透明度可能导致偏见或歧视的指控。因此，确保模型具有可解释性帮助组织对其自动化决策负责，促进公平和公正。此外，可解释性模型允许提高模型性能。当数据科学家能够理解模型如何得出结论时，他们可以识别改进的领域。例如，如果模型持续错误分类某些数据点，理解其决策过程可以帮助研究人员优化模型。这个迭代过程导致更强大和有效的算法，最终使最终用户受益。然而，实现可解释性并非没有挑战。在许多情况下，高度复杂的模型，如深度神经网络，提供了卓越的预测能力，但缺乏透明性。它们作为“黑箱”运作，使人们难以辨别输入是如何转化为输出的。因此，研究人员正在积极探索增强模型可解释性的方法。特征重要性分析、本地可解释模型无关的解释（LIME）和SHAP（SHapley Additive exPlanations）等技术正在被开发，以揭示复杂算法的内部工作原理。总之，随着我们在一个越来越依赖数据的环境中前行，可解释性模型的重要性不容小觑。它们使用户能够信任自动化系统，促进问责制，并提高模型性能。随着技术的不断进步，优先考虑可解释性将对确保数据驱动的决策公平、透明和有利于整个社会至关重要。通过努力实现可解释性模型，我们可以建立一个技术以清晰和诚信服务人类的未来。