LOGISTIC回归 英语是什么意思 - 英语翻译

• logistic regression

在 中文 中使用 Logistic回归 的示例及其翻译为 英语

现在让我们看看Logistic回归的成本函数。
Now let's look at the cost function for logistic regression.

那么Logistic回归模型可以表示为.
The logistic regression model can be.

你也可以认为Logistic回归是一个单层神经网络。
You can also think of Logistic regression as a one layered Neural Network.

什么是Logistic回归？?
What is a Logistic Regression?

和所有回归分析一样，logistic回归是一种预测性分析。
Like all linear regressions the logistic regression is a predictive analysis.

你也可以认为Logistic回归是一个单层神经网络。
We can think of logistic regression as one layer neural network.

基于Logistic回归模型的城镇?
Based on a logistic regression model?

和所有回归分析一样，logistic回归是一种预测性分析。
Like all regression analyses, the logistic regression is a predictive analysis.

然后，logistic回归模型的Newton-Raphson更新公式就变成了.
The Newton-Raphson update formula for the logistic regression model then becomes w(new).

Logistic回归模型是流行病学病因分析中最为普遍的方法。
Regression modelling is one of the most widely utilized approaches in epidemiological analyses.

像所有回归分析一样，Logistic回归是预测分析。
Like all linear regressions the logistic regression is a predictive analysis.

以此建模的广义线性模式通常称为logistic回归模式。
The generalized model formed in this way is called a logistic regression model.

现在，我们使用最大似然来确定logistic回归模型的参数。
We now use maximum likelihood to determine the parameters of the logistic regression model.

像所有回归分析一样，Logistic回归是预测分析。
Like all regression analyses, the logistic regression is a predictive analysis.

就像我已经提到的那样，Logistic回归通过线性边界将你的输入分成两个「区域」，每个类别划分一个区域。
Like I already mentioned, Logistic Regression separates your input into two„regions” by a linear boundary, one for each class.

在逐步、多元、logistic回归分析中，SIBO仍是冠心病的独立危险因素。
In the stepwise, multi-variant, logistic regression analysis, SIBO remained an independent risk factor for coronary artery disease.”.

例如，logistic回归建模p(y|x;θ)为hθ(x)=g(θTx)，其中g为sigmoid函数。
For instance, logistic regression modeled p( y| x; θ) as h θ( x)= g( θ T x) where g is the sigmoid function.

随机效应Logistic回归模型用于检验任何营销前特征与上市后安全行为是否相关。
A random effects logistic regression model was used to test whether any pre-marketing characteristics were associated with either post-marketing safety action.

Logistic回归：Logistic回归是一种使用一个或多个解释变量对二项式结果建模的强大统计方法。
Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables.

在Logistic回归中，为了计算输出（y=a），我们使用下面的计算图：.
In logistic regression, to calculate the output(y= a), we used the below computation graph:.

Logistic回归引入了伯努利分布（Bernoullidistribution）中的对数似然概念，并涉及到了一个称作sigmoid函数的简单变换。
Logistic Regression introduces the concept of the Log-Likelihood of the Bernoulli distribution, and covers a neat transformation called the sigmoid function.

在logistic回归中，输出是默认类别的概率（不像线性回归一样，输出是直接生成的）。
In logistic regression, the output is in the form of probabilities of the default class(unlike linear regression, where the output is directly produced).

Logistic回归并非最强大的算法之一，它可以很容易地被更为复杂的算法所超越。
Logistic Regression is also not one of the most powerful algorithms out there and can be easily outperformed by more complex ones.

Logistic回归是一个非常浅的模型，因为它只有一层（记住我们不把输入作为一个层来计算）：.
A logistic regression is a very shallow model as it has only one layer(remember we don't count the input as a layer):.

Logistic回归还可以帮助你理解预测结果背后起作用的因素，它不完全是一个黑盒方法。
Logistic regression can also help you understand the contributing factors behind the prediction, and is not just a black box method.

在这篇文章中，你将明白什么是Logistic回归、它是如何工作的、有哪些优缺点等等。
In this post, you will learn what Logistic Regression is, how it works, what are advantages and disadvantages and much more.

Logistic回归可以给出线性分类边界，所以如果你要使用它，你一定要确保你能接受线性的近似。
Logistic regression gives linear class boundaries, so when you use it, make sure a linear approximation is something you can live with.

与线性回归一样，Logistic回归是参数化机器学习的一个例子。
Like linear regression, logistic regression is an example of parametric machine learning.

作为组合预测模型建立的基础，本文首先分别建立线性回归和Logistic回归模型。
As the basis of the combination forecasting model, the linear regression and Logistic regression model are established, respectively at first.

在前一节中，我们利用自编码器来学习输入至softmax或logistic回归分类器的特征。
In the previous section, you used an autoencoder to learn features that were then fed as input to a softmax or logistic regression classifier.