regressor

简明释义

英[/rɪˈɡrɛsər/]美[/rɪˈɡrɛsər/]

n. [数] 回归量

英英释义

单词用法

同义词

反义词

例句

1.For increasing the speed of convergence of the signed regressor constant modulus algorithm (SRCMA), a fast SRCMA algorithm suitable for real BPSK signals is presented.

为了加快符号回归常数模算法(SRCMA)的收敛速度，本文提出了一种适合于BPS K信号的快速算法。

2.For increasing the speed of convergence of the signed regressor constant modulus algorithm (SRCMA), a fast SRCMA algorithm suitable for real BPSK signals is presented.

为了加快符号回归常数模算法(SRCMA)的收敛速度，本文提出了一种适合于BPS K信号的快速算法。

3.The condition, which they imposed on the distribution of the regressor variable X in their construction of the estimators, is very restrictive.

但在把他们的估计中，对自变量的分布加上了很强的限制性条件。

4.R2 generally increases when a regressor is added to a regression.

当回归中加入另外的解释变量时，R2通常会上升。

5.In machine learning, a regressor 回归器 is used to predict continuous values.

在机器学习中，regressor 回归器用于预测连续值。

6.The linear regressor 线性回归器 models the relationship between input features and output labels.

线性regressor 线性回归器建模输入特征与输出标签之间的关系。

7.We trained a decision tree regressor 决策树回归器 to handle non-linear data.

我们训练了一个决策树regressor 决策树回归器来处理非线性数据。

8.The performance of the regressor 回归器 can be evaluated using metrics like RMSE and R-squared.

可以使用RMSE和R平方等指标评估regressor 回归器的性能。

9.A support vector regressor 支持向量回归器 is effective for high-dimensional datasets.

支持向量regressor 支持向量回归器对高维数据集非常有效。

作文

In the realm of statistics and machine learning, understanding various concepts is crucial for effective data analysis. One such concept is the term regressor, which plays a significant role in predictive modeling. A regressor refers to a type of variable that is used to predict the value of another variable, often referred to as the dependent variable. In simpler terms, a regressor is an independent variable that helps in estimating outcomes based on its relationship with the dependent variable. To illustrate this further, consider a scenario where we want to predict a student's performance based on the number of hours they study. Here, the number of study hours serves as the regressor, while the student's performance, measured by their exam scores, is the dependent variable. By analyzing the relationship between these two variables, we can create a model that allows us to estimate how changes in study hours might impact exam scores. The use of regressors is not limited to academic performance; it extends to various fields such as economics, healthcare, and marketing. For instance, in economics, a common application involves predicting consumer spending based on income levels. In this case, income acts as a regressor, and consumer spending is the outcome we wish to predict. Similarly, in healthcare, researchers may utilize various factors such as age, diet, and exercise habits as regressors to predict health outcomes like the risk of developing certain diseases. Moreover, the effectiveness of a regressor can be evaluated using statistical techniques such as correlation and regression analysis. Correlation measures the strength and direction of the relationship between the regressor and the dependent variable, while regression analysis provides a more detailed understanding of how changes in the regressor affect the dependent variable. This analytical approach enables researchers and analysts to make informed decisions based on data-driven insights. However, it is essential to note that not all variables can serve as effective regressors. The choice of a regressor should be guided by theoretical understanding and empirical evidence. Additionally, multicollinearity, which occurs when two or more regressors are highly correlated, can lead to unreliable estimates and complicate the interpretation of results. Thus, careful selection and validation of regressors are paramount in any predictive modeling endeavor. In conclusion, the concept of a regressor is fundamental in the fields of statistics and machine learning. By serving as independent variables that help predict dependent variables, regressors enable analysts to uncover valuable insights from data. Whether applied in education, economics, or healthcare, the proper understanding and application of regressors can significantly enhance our ability to make predictions and inform decision-making processes. As we continue to navigate an increasingly data-driven world, mastering the concept of a regressor will undoubtedly be an asset for anyone involved in data analysis and predictive modeling.

在统计学和机器学习领域，理解各种概念对于有效的数据分析至关重要。其中一个重要的概念是术语regressor，它在预测建模中发挥着重要作用。regressor指的是一种用于预测另一个变量值的变量，通常称为因变量。简单来说，regressor是一个独立变量，它有助于根据与因变量的关系来估计结果。 为了进一步说明这一点，考虑一个场景，我们想根据学生学习的小时数来预测他们的表现。在这里，学习小时数充当regressor，而学生的表现通过考试成绩来衡量，是因变量。通过分析这两个变量之间的关系，我们可以创建一个模型，使我们能够估计学习小时数的变化可能如何影响考试成绩。 regressors的使用并不仅限于学业表现；它扩展到经济学、医疗保健和市场营销等各个领域。例如，在经济学中，一个常见的应用涉及根据收入水平预测消费者支出。在这种情况下，收入作为regressor，而消费者支出是我们希望预测的结果。同样，在医疗保健中，研究人员可能利用诸如年龄、饮食和锻炼习惯等各种因素作为regressors来预测健康结果，比如患某些疾病的风险。 此外，regressor的有效性可以通过相关性和回归分析等统计技术进行评估。相关性测量regressor与因变量之间关系的强度和方向，而回归分析则提供了更详细的理解，说明regressor的变化如何影响因变量。这种分析方法使研究人员和分析师能够根据数据驱动的洞察做出明智的决策。 然而，值得注意的是，并非所有变量都能作为有效的regressors。选择regressor应受到理论理解和实证证据的指导。此外，多重共线性，即两个或多个regressors高度相关，可能导致不可靠的估计，并使结果的解释复杂化。因此，在任何预测建模工作中，仔细选择和验证regressors至关重要。 总之，regressor的概念在统计学和机器学习领域是基础。通过充当帮助预测因变量的独立变量，regressors使分析师能够从数据中发现有价值的洞察。无论是在教育、经济学还是医疗保健中，正确理解和应用regressors都能显著增强我们进行预测和为决策过程提供信息的能力。随着我们继续在一个日益数据驱动的世界中航行，掌握regressor的概念无疑将成为任何参与数据分析和预测建模的人的一项资产。