What is the translation of " 线性回归模型 " in English?

• linear regression models
• linear regression model
• the linearregression model

Examples of using 线性回归模型 in Chinese and their translations into English

最简单的线性回归模型形式也是输入变量的线性函数。
The simplest form of linear regression models are linear functions of the input variables.

如何评估逻辑回归与简单线性回归模型预测的性能？?
How do you assess logistic regression versus simple linear regression models?

多元线性回归模型如下：.
Multiple linear regression model is as follows:.

这样的模型被称为多元线性回归模型。
Such kind of model is known as a multivariate or multiple linear regression model.

该问题也被称为多元线性回归模型。
The Polynomial regression is also called as multiple linear regression models.

这样的模型被称为多元线性回归模型。
This model is known as a multiple linear regression model.

它还通过提高准确性来帮助线性回归模型。
It also helps linear regression models by improving their accuracy.

现在，我们来构建一个简单的线性回归模型。
So now, we have introduced our simple linear regression model.

建立以下多元线性回归模型:.
Construct the following multiple linear regression model:.

有许多方法可以测量线性回归模型的准确性。
There are many ways to measure the accuracy of a linear regression model.

线性回归模型将尝试绘制直线拟合数据：.
A linear regression model will try to draw a straight line to fit the data:.

线性回归模型的基本假设.
Basic Assumptions of a Linear Regression Model.

一元线性回归模型的基本假设.
Basic Assumptions of a Linear Regression Model.

线性回归模型的基本假设。
The Assumptions of the Linear Regression Model.

线性回归模型的最后一步是找出RMSE。
The last step in the linear regression model is to find out the RMSE.

线性回归模型有一种特殊的形式。
A linear regression model follows a very particular form.

那么，我们建立线性回归模型。
Suppose we have a linear regression model.

通过L2损失最小化进行训练的线性回归模型。
A linear regression model trained by minimizing L2 Loss.

此外，它能够减少变化性和提高线性回归模型的准确性。
It reduces the variability and improves the accuracy of linear regression models.

线性回归模型的最简单的形式也是输入变量的线性函数。
The simplest form of linear regression models are linear functions of the input variables.

线性回归模型对象为我们存储这些值，让我们看一看。
The LinearRegression model object stores those values for us, so let's take a look.

在第二章中，我们根据类别指示变量拟合线性回归模型，并将观测点分到拟合值最大的类别。
In Chapter 2 we fit linear regression models to the class indicator variables, and classify to the largest fit.

线性回归模型适合于来自对北部(北纬35°－55°)和南部(北纬25°－35°)地带的地中海案例研究的样品。
Linear regression models were fitted to the samples from a Mediterranean case study in the northern(35o-55o N) and southern(25o-35o N) belts.

将自变量添加到线性回归模型将总是增加模型的解释方差（通常表示为R2）。
Adding independent variables to a multiple linear regression model will always increase the amount of explained variance in the dependent variable(typically expressed as R²).

我们通过使用线性回归模型类中的“predict”函数来实现这一点。
We do that by using the'predict' function within the LinearRegression model class.

例如，线性回归模型通常将均方误差用作损失函数，而逻辑回归模型则使用对数损失函数。
For example, linear regression models typically use mean squared error for a loss function, while logistic regression models use Log Loss.

这违反了拟合简单线性回归模型需满足的假设之一。
This violates one of the assumptions required for fitting a simple linear regression model.

这些额外的因素被称为Fama-French因素，以发展多元线性回归模型的教授命名，以更好地解释资产回报。
These additional factors are known as the Fama-French factors, named after the professors who developed the multiple linear regression model to better explain asset returns.

相比之下，线性回归模型和宽度模型的可解释性通常要好得多。
By contrast, linear regression models and wide models are typically far more interpretable.