它们在解决解决方案方面非常相似，但正如其他人所说，一个(逻辑回归)是用于预测类别“适合”(Y/N或1/0)，另一个(线性回归)是用于预测值。

所以如果你想预测你是否有癌症Y/N(或概率)-使用逻辑。如果你想知道你能活多少年，用线性回归吧!

在线性回归的情况下，结果是连续的，而在逻辑回归的情况下，结果是离散的(非连续的)

要执行线性回归，我们需要因变量和自变量之间的线性关系。但要执行逻辑回归，我们不需要因变量和自变量之间的线性关系。

线性回归是在数据中拟合一条直线，而逻辑回归是在数据中拟合一条曲线。

线性回归是机器学习的一种回归算法，逻辑回归是机器学习的一种分类算法。

线性回归假设因变量呈高斯(或正态)分布。逻辑回归假设因变量为二项分布。

逻辑回归用于预测分类输出，如是/否，低/中/高等。你基本上有2种类型的逻辑回归二元逻辑回归(是/否，批准/不批准)或多类逻辑回归(低/中/高，0-9等数字)

另一方面，线性回归是因变量(y)是连续的。 Y = mx + c是一个简单的线性回归方程(m =斜率，c是Y截距)。多元线性回归有不止一个自变量(x1,x2,x3，…)等)

Regression means continuous variable, Linear means there is linear relation between y and x. Ex= You are trying to predict salary from no of years of experience. So here salary is independent variable(y) and yrs of experience is dependent variable(x). y=b0+ b1*x1 We are trying to find optimum value of constant b0 and b1 which will give us best fitting line for your observation data. It is a equation of line which gives continuous value from x=0 to very large value. This line is called Linear regression model.

逻辑回归是一种分类技术。不要被术语回归所误导。这里我们预测y=0还是1。

在这里，我们首先需要从下面的公式中找出给定x的p(y=1) (y=1的w概率)。

概率p通过下面的公式与y相关

Ex=我们可以将患癌几率超过50%的肿瘤分类为1，将患癌几率低于50%的肿瘤分类为0。

这里红点被预测为0，而绿点被预测为1。

Linear regression output as probabilities It's tempting to use the linear regression output as probabilities but it's a mistake because the output can be negative, and greater than 1 whereas probability can not. As regression might actually produce probabilities that could be less than 0, or even bigger than 1, logistic regression was introduced. Source: http://gerardnico.com/wiki/data_mining/simple_logistic_regression Outcome In linear regression, the outcome (dependent variable) is continuous. It can have any one of an infinite number of possible values. In logistic regression, the outcome (dependent variable) has only a limited number of possible values. The dependent variable Logistic regression is used when the response variable is categorical in nature. For instance, yes/no, true/false, red/green/blue, 1st/2nd/3rd/4th, etc. Linear regression is used when your response variable is continuous. For instance, weight, height, number of hours, etc. Equation Linear regression gives an equation which is of the form Y = mX + C, means equation with degree 1. However, logistic regression gives an equation which is of the form Y = eX + e-X Coefficient interpretation In linear regression, the coefficient interpretation of independent variables are quite straightforward (i.e. holding all other variables constant, with a unit increase in this variable, the dependent variable is expected to increase/decrease by xxx). However, in logistic regression, depends on the family (binomial, Poisson, etc.) and link (log, logit, inverse-log, etc.) you use, the interpretation is different. Error minimization technique Linear regression uses ordinary least squares method to minimise the errors and arrive at a best possible fit, while logistic regression uses maximum likelihood method to arrive at the solution. Linear regression is usually solved by minimizing the least squares error of the model to the data, therefore large errors are penalized quadratically. Logistic regression is just the opposite. Using the logistic loss function causes large errors to be penalized to an asymptotically constant. Consider linear regression on categorical {0, 1} outcomes to see why this is a problem. If your model predicts the outcome is 38, when the truth is 1, you've lost nothing. Linear regression would try to reduce that 38, logistic wouldn't (as much)2.

基本区别:

线性回归基本上是一个回归模型，这意味着它将给出一个函数的非离散/连续输出。这个方法给出了值。例如，给定x, f(x)是多少

例如，给定一个由不同因素组成的训练集和训练后的房地产价格，我们可以提供所需的因素来确定房地产价格。

逻辑回归基本上是一种二元分类算法，这意味着这里函数的输出值是离散的。例如:对于给定的x，如果f(x)>阈值将其分类为1，否则将其分类为0。

例如，给定一组脑瘤大小作为训练数据，我们可以使用大小作为输入来确定它是良性肿瘤还是恶性肿瘤。因此这里的输出不是0就是1。

这里的函数基本上是假设函数