blocks|key|490702|text|简单地说，线性回归是一种回归算法，它输出一个可能的连续和无穷大的值；logistic回归被认为是一种二分类算法，它输出属于标签(0或1)的输入的“概率”。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|490703|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Simply put, linear regression is a regression algorithm, which outpus a possible continous and infinite value; logistic regression is considered as a binary classifier algorithm, which outputs the 'probability' of the input belonging to a label (0 or 1).

blocks|key|273711|text|最基本的区别是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|273712|线性回归基本上是一种回归模型，这意味着它将给出一个函数的非离散/连续输出。因此，这种方法给出了价值。例如:给定x，f(x)是什么？|273713|例如，给定不同因素的训练集和训练后的物业价格，我们可以提供所需的因素来确定物业价格。|273714|逻辑回归基本上是一种二进制分类算法，这意味着在这里，函数将有离散的值输出。例如:对于给定的x，如果f(X)>阈值，则将其分类为1，否则将其分类为0。|273715|例如，给定一组脑瘤大小作为训练数据，我们可以使用该大小作为输入，以确定它是良性肿瘤还是恶性肿瘤。因此，这里的输出是谨慎的，不是0就是1。|273716|*这里的函数基本上是假设函数|273717|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|O|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|P|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|Q|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|S|8|@]|9|@]|A|$]]|$1|L|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|M|$]]

The basic difference :

Linear regression is basically a regression model which means its will give a non discreet/continuous output of a function. So this approach gives the value. For example : given x what is f(x)

For example given a training set of different factors and the price of a property after training we can provide the required factors to determine what will be the property price.

Logistic regression is basically a binary classification algorithm which means that here there will be discreet valued output for the function . For example : for a given x if f(x)>threshold classify it to be 1 else classify it to be 0.

For example given a set of brain tumour size as training data we can use the size as input to determine whether its a benine or malignant tumour. Therefore here the output is discreet either 0 or 1.

*here the function is basically the hypothesis function

blocks|key|273755|text|它们在解决方案方面都非常相似，但正如其他人所说，一个(Logistic回归)用于预测类别“匹配”(Y/N或1/0)，另一个(线性回归)用于预测一个值。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|273756|因此，如果你想预测你是否患有癌症Y/N+(或概率)-使用逻辑分析。如果你想知道你能活多少年--使用线性回归！|273757|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

They are both quite similar in solving for the solution, but as others have said, one (Logistic Regression) is for predicting a category "fit" (Y/N or 1/0), and the other (Linear Regression) is for predicting a value.

So if you want to predict if you have cancer Y/N (or a probability) - use logistic. If you want to know how many years you will live to - use Linear Regression !

blocks|key|491228|text|回归意味着连续变量，线性意味着y和x之间存在线性关系。Ex=你正试图根据没有经验的经验来预测工资。所以这里工资是自变量(Y)，年资是因变量(X)。y=b0%2B+b1*x1|type|unstyled|depth|inlineStyleRanges|entityRanges|data|491229|📷|atomic|offset|length|491230|我们正在努力寻找常数b0和b1的最佳值，这将为您的观测数据提供最佳的拟合线。这是一个直线方程，它给出了从x=0到非常大的连续值。这条线被称为线性回归模型。|491231|Logistic回归是一种分类技术。不要被术语回归所误导。这里我们预测是y=0还是1。|491232|这里我们首先需要从下面的公式中找到给定x的p(y=1)+(y=1的w概率)。|491233|​|491234|491235|491236|概率p与y的关系式如下：|491237|491238|491239|491240|Ex=we可以将发病几率大于50%25的肿瘤分类为1，将发病几率小于50%25的肿瘤分类为0。|491241|491242|491243|在这里，红点将被预测为0，而绿点将被预测为1。|491244|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/ed555ea73f30802133a2a3f1cec2c778.png|imageAlt|1|https://ask.qcloudimg.com/http-save/yehe-900000/aae5d0aaf76f071c4f1fe5c7fb4e08a2.png|2|https://ask.qcloudimg.com/http-save/yehe-900000/81ad5c2cd946fff6df792deccf7e10f6.png|3|https://ask.qcloudimg.com/http-save/yehe-900000/f74a25ea674cbbc9c96c17a2d861fb25.png^0|0|0|1|0|0|0|0|0|0|0|1|1|0|0|0|0|0|1|2|0|0|0|0|1|3|0|0|0^^$0|@$1|2|3|4|5|6|7|1G|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|1H|8|@]|9|@$E|1I|F|1J|1|1K]]|A|$]]|$1|G|3|H|5|6|7|1L|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1M|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|1N|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|1O|8|@]|9|@]|A|$]]|$1|O|3|C|5|D|7|1P|8|@]|9|@$E|1Q|F|1R|1|1S]]|A|$]]|$1|P|3|N|5|6|7|1T|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1U|8|@]|9|@]|A|$]]|$1|S|3|N|5|6|7|1V|8|@]|9|@]|A|$]]|$1|T|3|C|5|D|7|1W|8|@]|9|@$E|1X|F|1Y|1|1Z]]|A|$]]|$1|U|3|N|5|6|7|20|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|21|8|@]|9|@]|A|$]]|$1|X|3|C|5|D|7|22|8|@]|9|@$E|23|F|24|1|25]]|A|$]]|$1|Y|3|N|5|6|7|26|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|27|8|@]|9|@]|A|$]]|$1|11|3|-4|5|6|7|28|8|@]|9|@]|A|$]]]|12|$13|$5|14|15|16|A|$17|18|19|-4]]|1A|$5|14|15|16|A|$17|1B|19|-4]]|1C|$5|14|15|16|A|$17|1D|19|-4]]|1E|$5|14|15|16|A|$17|1F|19|-4]]]]

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
<a href="https://i.stack.imgur.com/kW8YP.png" rel="noreferrer"><img src="https://i.stack.imgur.com/kW8YP.png" alt="Linear regression"></a>
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.

Logistic regression is type of classification technique. Dnt be misled by term regression. Here we predict whether y=0 or 1.

Here we first need to find p(y=1) (wprobability of y=1) given x from formuale below.

<a href="https://i.stack.imgur.com/pTIw4.png" rel="noreferrer"><img src="https://i.stack.imgur.com/pTIw4.png" alt="prob"></a>

Probaibility p is related to y by below formuale

<a href="https://i.stack.imgur.com/qllEd.png" rel="noreferrer"><img src="https://i.stack.imgur.com/qllEd.png" alt="s"></a>

Ex=we can make classification of tumour having more than 50% chance of having cancer as 1 and tumour having less than 50% chance of having cancer as 0.
<a href="https://i.stack.imgur.com/fKW8B.png" rel="noreferrer"><img src="https://i.stack.imgur.com/fKW8B.png" alt="5"></a>

Here red point will be predicted as 0 whereas green point will be predicted as 1.

blocks|key|490949|text|我不能完全同意上面的评论。除此之外，还有一些不同之处，比如|type|unstyled|depth|inlineStyleRanges|entityRanges|data|490950|在线性回归中，假设残差是正态分布的。在Logistic回归中，残差需要是独立的，但不是正态分布的。|490951|线性回归假设解释变量的值的恒定变化导致响应变量的恒定变化。如果响应变量的值表示概率(在Logistic回归中)，则此假设不成立|490952|广义线性模型(GLM)不假定因变量和自变量之间存在线性关系。然而，在logit模型中，它假设连接函数与自变量之间存在线性关系。|490953|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

Cannot agree more with the above comments. 
Above that, there are some more differences like

In Linear Regression, residuals are assumed to be normally distributed. 
In Logistic Regression, residuals need to be independent but not normally distributed. 

Linear Regression assumes that a constant change in the value of the explanatory variable results in constant change in the response variable. 
This assumption does not hold if the value of the response variable represents a probability (in Logistic Regression)

GLM(Generalized linear models) does not assume a linear relationship between dependent and independent variables. However, it assumes a linear relationship between link function and independent variables in logit model.

blocks|key|490914|text|简而言之:线性回归提供连续的输出。即一系列值之间的任意值。Logistic回归给出离散输出。即是/否，0/1种输出。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|490915|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

In short:
Linear Regression gives continuous output. i.e. any value between a range of values.
Logistic Regression gives discrete output. i.e. Yes/No, 0/1 kind of outputs.

blocks|key|273914|text|%7C+Basis+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C+Linear+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C+Logistic++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C
%7C-----------------------------------------------------------------%7C--------------------------------------------------------------------------------%7C---------------------------------------------------------------------------------------------------------------------%7C
%7C+Basic+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C+The+data+is+modelled+using+a+straight+line.++++++++++++++++++++++++++++++++++++%7C+The+probability+of+some+obtained+event+is+represented+as+a+linear+function+of+a+combination+of+predictor+variables.+%7C
%7C+Linear+relationship+between+dependent+and+independent+variables+%7C+Is+required++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C+Not+required++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++%7C
%7C+The+independent+variable++++++++++++++++++++++++++++++++++++++++%7C+Could+be+correlated+with+each+other.+(Specially+in+multiple+linear+regression)+%7C+Should+not+be+correlated+with+each+other+(no+multicollinearity+exist).++++++++++++++++++++++++++++++++++++++++++++++%7C|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|273915|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>| Basis | Linear | Logistic |
|-----------------------------------------------------------------|--------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Basic | The data is modelled using a straight line. | The probability of some obtained event is represented as a linear function of a combination of predictor variables. |
| Linear relationship between dependent and independent variables | Is required | Not required |
| The independent variable | Could be correlated with each other. (Specially in multiple linear regression) | Should not be correlated with each other (no multicollinearity exist). |
</code></pre>

blocks|key|273910|text|简单地说，如果在线性回归模型中有更多的测试用例到达，而这些测试用例远离y=1和y=0的预测阈值(比如=0.5)。然后，在这种情况下，假设将改变，并成为worse.Therefore线性回归模型不用于分类问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|273911|另一个问题是，如果分类是y=0和y=1，h(x)可以>1或<0，因此我们使用0<=h(x)<=1的Logistic回归。|273912|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

To put it simply, if in linear regression model more test cases arrive which are far away from the threshold(say =0.5)for a prediction of y=1 and y=0. Then in that case the hypothesis will change and become worse.Therefore linear regression model is not used for classification problem.

Another Problem is that if the classification is y=0 and y=1, h(x) can be > 1 or &lt; 0.So we use Logistic regression were 0&lt;=h(x)&lt;=1.

blocks|key|491060|text|逻辑回归用于预测分类输出，如是/否，低/中/高等。你基本上有两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，数字从0-9等)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|491061|另一方面，线性回归是指因变量(y)是连续的。Y=+mx+%2Bc是一个简单的线性回归方程(m+=斜率，c是y截距)。多元线性回归有一个以上的自变量(x1，x2，x3…等)|491062|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Logistic Regression is used in predicting categorical outputs like Yes/No, Low/Medium/High etc. You have basically 2 types of logistic regression Binary Logistic Regression (Yes/No, Approved/Disapproved) or Multi-class Logistic regression (Low/Medium/High, digits from 0-9 etc)

On the other hand, linear regression is if your dependent variable (y) is continuous. 
y = mx + c is a simple linear regression equation (m = slope and c is the y-intercept). Multilinear regression has more than 1 independent variable (x1,x2,x3 ... etc)

blocks|key|491367|text|在线性回归中，结果是连续的，而在逻辑回归中，结果只有有限数量的可能值(离散)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|491368|例如:在一个场景中，x的给定值是以平方英尺为单位的地块的大小，然后预测该地块的the比率属于线性回归。|491369|相反，如果你想根据大小预测地块是否会卖到300000卢比以上，你可以使用逻辑回归。可能的输出要么是Yes，plot将以超过300000卢比的价格出售，要么是No。|491370|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

In linear regression the outcome is continuous whereas in logistic regression, the outcome has only a limited number of possible values(discrete).

example:
In a scenario,the given value of x is size of a plot in square feet then predicting y ie rate of the plot comes under linear regression. 

If, instead, you wanted to predict, based on size, whether the plot would sell for more than 300000 Rs, you would use logistic regression. The possible outputs are either Yes, the plot will sell for more than 300000 Rs, or No.

blocks|key|274503|text|在线性回归的情况下，结果是连续的，而在Logistic回归的情况下，结果是离散的(不连续)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|274504|要执行线性回归，我们需要在因变量和自变量之间建立线性关系。但是，要执行Logistic回归，我们不需要因变量和自变量之间的线性关系。|274505|线性回归就是在数据中拟合一条直线，而Logistic回归就是在数据中拟合曲线。|274506|线性回归是机器学习的回归算法，而Logistic回归是机器学习的分类算法。|274507|线性回归假设因变量呈高斯(或正态)分布。Logistic回归假设因变量为二项分布。|274508|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|N|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|O|8|@]|9|@]|A|$]]|$1|H|3|I|5|6|7|P|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|K|$]]

In case of Linear Regression the outcome is continuous while in case of Logistic Regression outcome is discrete (not continuous)

To perform Linear regression we require a linear relationship between the dependent and independent variables. But to perform Logistic regression we do not require a linear relationship between the dependent and independent variables.

Linear Regression is all about fitting a straight line in the data while Logistic Regression is about fitting a curve to the data.

Linear Regression is a regression algorithm for Machine Learning while Logistic Regression is a classification Algorithm for machine learning.

Linear regression assumes gaussian (or normal) distribution of dependent variable. Logistic regression assumes binomial distribution of dependent variable.

When we have to predict the value of a <a href="https://en.wikipedia.org/wiki/Categorical_variable" rel="noreferrer">categorical</a> (or discrete) outcome we use <a href="https://en.wikipedia.org/wiki/Logistic_regression" rel="noreferrer">logistic regression</a>. I believe we use <a href="https://en.wikipedia.org/wiki/Linear_regression" rel="noreferrer">linear regression</a> to also predict the value of an outcome given the input values.

Then, what is the difference between the two methodologies?

What is the difference between linear regression and logistic regression?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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当我们必须预测 (或离散)结果的值时，我们使用。我相信我们也可以使用来预测给定输入值的结果的值。那么，这两种方法有什么不同呢？

问线性回归和逻辑回归的区别是什么？
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问线性回归和逻辑回归的区别是什么？EN

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问线性回归和逻辑回归的区别是什么？
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