问题描述
根据我所了解的，线性回归预测的结果可以具有连续值，而逻辑回归预测的结果是离散的.在我看来，逻辑回归类似于分类问题.那么，为什么叫回归?
According to what I have understood, linear regression predicts the outcome which can have continuous values, whereas logistic regression predicts outcome which is discrete. It seems to me that logistic regression is similar to a classification problem. So, why is it called regression?
还有一个相关的问题:线性回归和逻辑回归之间有什么区别?
There is also a related question: What is the difference between linear regression and logistic regression?
推荐答案
线性回归和逻辑回归之间有严格的联系.
There is a strict link between linear regression and logistic regression.
通过线性回归，您正在寻找k 个参数:
With linear regression you're looking for the k parameters:
使用逻辑回归可以达到相同的目标，但是等式是:
With logistic regression you've the same aim but the equation is:
g是 Sigmoid函数的地方:
所以:
您需要将K拟合到您的数据中.
and you need to fit K to your data.
假设存在二元分类问题，则输出h是示例x在分类任务中为正匹配的估计概率:
Assuming a binary classification problem, the output h is the estimated probability that the example x is a positive match in the classification task:
当概率大于0.5时，我们可以预测匹配".
When the probability is greater than 0.5 then we can predict "a match".
在以下情况下，概率大于0.5:
The probability is greater than 0.5 when:
并且在以下情况下是正确的
and this is true when:
超平面:
是决策边界.
总结:
- 逻辑回归是使用相同线性回归基本公式的广义线性模型，但它是回归得出绝对结果的可能性.
- logistic regression is a generalized linear model using the same basic formula of linear regression but it is regressing for the probability of a categorical outcome.
这是一个非常删节的版本.您可以在这些视频(机器学习的第三周，由Andrew Ng提供.
This is a very abridged version. You can find a simple explanation in these videos (third week of Machine Learning by Andrew Ng).
您还可以查看 http://www.holehouse.org/mlclass/06_Logistic_Regression .html 中有关课程的一些注释.
You can also take a look at http://www.holehouse.org/mlclass/06_Logistic_Regression.html for some notes on the lessons.
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