What is the translation of " 逻辑回归模型 " in English?

• logistic regression model
• logistic regression models

Examples of using 逻辑回归模型 in Chinese and their translations into English

创建逻辑回归模型。
Creates a new Logistic Regression Model.

多项逻辑回归模型应用于10个类的子集。
A multinomial logistic regression model was applied to a subset of 10 classes.

设计条件逻辑回归模型和生存分析。
DESIGN: Conditional logistic regression models and survival analysis.

逻辑回归模型可表示为.
The logistic regression model can be.

线性模型或者逻辑回归模型系数的绝对值解释为特征重要性.
Interpreting absolute value of coefficients from linear or logistic regression as feature importance.

逻辑回归模型也类似：.
The logistic regression model equals:.

这便是逻辑回归模型的代价函数了。
This is the logistic regression cost function.

逻辑回归模型的基本形式为：.
The specific form of the logistic regression model is:.

我们改变公式，我们可以得到逻辑回归模型：.
And we transform the formula, we can get the logistic regressions model:.

当你学习了一个支持向量机模型或逻辑回归模型（或者任何一个线性模型）之后，实际评估就会非常快。
After you have learned an SVM model or logistic regression model- or any linear model- the actual evaluation is very fast.

总的来说，群落动力学的条件逻辑回归模型表明，微生物组稳定性与IA或T1D的发病没有密切关系。
Overall, the conditional logistic regression models of community dynamics suggest that microbiome stability was not strongly related to the onset of IA or T1D.

最优的逻辑回归模型预测的平均概率等于训练数据的平均标签。
The average probability predicted by the optimal logistic regression model is equal to the average label on the training data.

使用多级逻辑回归模型确定糖尿病和前驱糖尿病的风险因素，在家庭和社区范围内进行群集调整。
Risk factors for diabetes and prediabetes were identified using multilevel logistic regression models, with adjustment for clustering within households and communities.

多项逻辑回归模型的AUC评分为0.82，而2D-CNN的准确度为0.94.
Multinomial logistic regression model gave an AUC score of 0.82, while a 2D-CNN showed an accuracy of 0.94.

通过多变量逻辑回归模型，非匹配和倾向评分（ps）匹配，用于检查胆固醇水平和认知功能之间的关联。
Multivariate logistic regression models, non-matched and propensity score(PS) matched, were used to examine the association between cholesterol levels and cognitive function.

第五步：最后，我们调用逻辑回归模型中的转换方法来用测试数据做预测。
Step 5: Finally, we can call the transform method in logistic regression model to make the predictions on the test data.

在这篇文章中，我将拟合一个二元逻辑回归模型并解释每一步。
In this post, I am going to fit a binary logistic regression model and explain each step.

第一个模型使用原始字节n-gram作为逻辑回归模型的输入特征[Raffetal.2016]。
The first of those models uses raw byte n-grams as the input features to a logistic regression model[Raff et al. 2016].

逻辑回归模型的质量由拟合度量和预测能力决定。
The quality of a logistic regression model is determined by measures of fit and predictive power.

采用逻辑回归模型确定与12个月恢复工作相关的因素。
A logistic regression model was fitted to identify factors associated with returning to work at 12 months.

逻辑回归模型可预测作为自变量函数值的因变量的概率。
A logistic regression model estimates the probability of a dependent variable as a function of independent variables.

把线性模型或者逻辑回归模型系数的绝对值解释为特征重要性.
Interpreting absolute value of coefficients from linear or logistic regression as feature importance.

利用上述公式，我们将数据拟合到逻辑回归模型中。
Using the formula illustrated above, we fit the data into the logistic regression model.

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.

它比逻辑回归模型具有更高的精度。
Had a higher accuracy than the linear single regression model.

简单起见，我们假设逻辑回归模型只有两个参数：权重w和偏差b。
For the sake of simplicity, let's assume that the logistic regression model has only two parameters: weight w and bias b.

在这段视频中，我们要介绍如何拟合逻辑回归模型的参数θ。
In this post I will show how to build a linear regression model.

摘要：我们使用线性或者逻辑回归模型来开发精确模型，为了预测相关的输出结果。
We use linear or logistic regression technique for developing accurate models for predicting an outcome of interest.

如果与标明损失类型的索赔比较，该逻辑回归模型的预测准确率是92%。
When compared with the claims for which the types of loss were indicated, the prediction success rate of this logistic regression model was 92 per cent.

WEB经过几年的数据收集，您将有足够的观察来创建模型-在这种情况下的逻辑回归模型。
After a couple of years of data collection, you will have enough observations to create a model- a logistic regression model in this case.