parameterize

简明释义

英[pəˈræmɪt(ə)raɪz]美[pəˈræmɪtəˌraɪz]

vt. 用参数表示；确定……的参数

第 三 人 称 单 数 p a r a m e t e r i z e s

现 在 分 词 p a r a m e t e r i z i n g

过 去 式 p a r a m e t e r i z e d

过 去 分 词 p a r a m e t e r i z e d

英英释义

单词用法

同义词

反义词

例句

1.But, I think here, we will get the simplest answer if we parameterize things by the Angle.

但如果我们使用角度作为参变量的话，我们可以得到最简单的答案。

2.Parameterize representations to identify links embedded within them.

对表示进行参数化，以标识内嵌于它们中的链接。

3.Operations and parameters variability-you can parameterize operations and parameters at the interface definition level.

操作和参数可变性——您可以在接口定义级别参数化操作和参数。

4.You can also parameterize as many aspects as you wish. Try adding color or ruler height as parameters, for example.

您还可以任意添加多个参数，例如，尝试添加颜色或标尺高度参数。

5.Cloud optical thickness and effective particle radius are the important parameters to parameterize the cloud optical properties.

云的光学厚度与有效粒子半径是云光学属性参数化方案中的重要参数。

6.OK, so the way we compute it is we take this expression Mdx Ndy and we parameterize the curve.

计算它的方式是找到Mdx和Ndy的表达式,然后把曲线参数化。

7.To optimize the algorithm, we should parameterize 参数化 the learning rate.

为了优化算法，我们应该对学习率进行参数化。

8.The software allows users to parameterize 参数化 their settings for better performance.

该软件允许用户对其设置进行参数化以获得更好的性能。

9.In order to make the model flexible, we can parameterize 参数化 it by introducing additional variables.

为了使模型灵活，我们可以通过引入额外的变量来参数化它。

10.We need to parameterize 参数化 the function to allow for different input values.

我们需要对这个函数进行参数化，以允许不同的输入值。

11.When developing simulations, it is crucial to parameterize 参数化 the input parameters accurately.

在开发模拟时，准确地参数化输入参数是至关重要的。

作文

In the world of data science and machine learning, the ability to effectively analyze and manipulate data is crucial. One of the key concepts that emerge in this field is the need to parameterize models. To parameterize means to express a system or model in terms of parameters, which are variables that can be adjusted to fit the model to the data. This process allows for greater flexibility and accuracy in predictions and analyses.When we parameterize a model, we set specific values for certain variables, enabling us to understand how changes in these variables affect the outcome of the model. For instance, in regression analysis, we might parameterize our model by defining coefficients for each predictor variable. These coefficients help us understand the relationship between the independent variables and the dependent variable we are trying to predict.Parameterization is not limited to regression; it applies to various types of models, including neural networks, decision trees, and more. In a neural network, for example, we parameterize the network by defining weights and biases for each neuron. Adjusting these parameters during the training process allows the network to learn from the data and improve its performance over time.Moreover, parameterizing a model can lead to more efficient computations. By reducing the number of variables that need to be considered, we can simplify complex models and make them more manageable. This is particularly important in large datasets, where computational resources can be a limiting factor. By focusing on key parameters, we can streamline our analyses and derive insights more quickly.Another significant aspect of parameterization is its role in model validation. Once we have parameterized a model, we can assess its performance using various metrics. This evaluation helps us determine whether our model is accurately capturing the underlying patterns in the data or if adjustments need to be made. If the model performs poorly, we may need to revisit our parameterization strategy and consider alternative approaches or additional parameters.In practice, parameterizing a model involves a combination of domain knowledge and statistical techniques. Understanding the context of the data is essential for selecting the right parameters and ensuring that the model is meaningful. Additionally, techniques such as cross-validation can aid in identifying the optimal set of parameters that yield the best predictive performance.In conclusion, the ability to parameterize models is a fundamental skill in data science and machine learning. It allows us to create flexible, accurate, and efficient models that can adapt to the complexities of real-world data. As we continue to advance in these fields, mastering the art of parameterization will be increasingly important for deriving actionable insights and making informed decisions based on data. Whether we are working with simple linear models or complex neural networks, understanding how to parameterize effectively will enhance our analytical capabilities and drive innovation in our approaches to solving problems.

在数据科学和机器学习的世界中，有效分析和处理数据的能力至关重要。这个领域中出现的一个关键概念是需要对模型进行参数化。参数化意味着用参数（可以调整以使模型适应数据的变量）来表示系统或模型。这个过程使得预测和分析更加灵活和准确。当我们对模型进行参数化时，我们为某些变量设置特定的值，使我们能够理解这些变量的变化如何影响模型的结果。例如，在回归分析中，我们可能会通过为每个预测变量定义系数来对模型进行参数化。这些系数帮助我们理解自变量与我们试图预测的因变量之间的关系。参数化不仅限于回归；它适用于各种类型的模型，包括神经网络、决策树等。在神经网络中，例如，我们通过为每个神经元定义权重和偏置来对网络进行参数化。在训练过程中调整这些参数使网络能够从数据中学习并随着时间的推移提高其性能。此外，参数化模型还可以导致更高效的计算。通过减少需要考虑的变量数量，我们可以简化复杂模型，使其更易于管理。这在大型数据集中特别重要，因为计算资源可能成为限制因素。通过关注关键参数，我们可以简化分析并更快地得出洞察。参数化的另一个重要方面是它在模型验证中的作用。一旦我们对模型进行了参数化，就可以使用各种指标评估其性能。这种评估帮助我们确定模型是否准确捕捉到数据中的基本模式，或者是否需要进行调整。如果模型表现不佳，我们可能需要重新审视我们的参数化策略，考虑替代方法或额外的参数。在实践中，对模型进行参数化涉及领域知识和统计技术的结合。理解数据的上下文对于选择正确的参数并确保模型具有意义至关重要。此外，交叉验证等技术可以帮助识别最佳参数集，从而获得最佳预测性能。总之，能够对模型进行参数化是数据科学和机器学习中的一项基本技能。它使我们能够创建灵活、准确且高效的模型，能够适应现实数据的复杂性。随着我们在这些领域的不断进步，掌握参数化的艺术将对从数据中获得可操作的洞察和做出明智的决策变得越来越重要。无论我们是使用简单的线性模型还是复杂的神经网络，理解如何有效地进行参数化将增强我们的分析能力，并推动我们在解决问题的方法上的创新。