2024年4月15日发(作者：)
分类目标的逻辑回归
英文回答：
Logistic regression is a statistical model that is used
to predict the probability of an event occurring. It is a
type of binary classification model, which means that it
can predict whether an instance belongs to one of two
classes.
Logistic regression is used in a wide variety of
applications, including:
Predicting the probability of a customer clicking on
an ad.
Predicting the probability of a patient having a
disease.
Predicting the probability of a student passing a test.
Predicting the probability of a loan applicant
defaulting on a loan.
Logistic regression is a relatively simple model to
understand and implement. It is also computationally
efficient, which makes it suitable for large datasets.
The logistic regression model is a sigmoid function,
which is a smooth, S-shaped curve. The sigmoid function
takes a real-valued input and outputs a probability between
0 and 1.
The logistic regression model is fitted to data using a
maximum likelihood estimation procedure. This procedure
finds the values of the model's parameters that maximize
the likelihood of the observed data.
Once the logistic regression model is fitted, it can be
used to predict the probability of an event occurring for
new data. The model can also be used to calculate the odds
ratio, which is the ratio of the odds of an event occurring
for two different groups.
中文回答：
逻辑回归是一种用于预测事件发生概率的统计模型。它是一种
二元分类模型，也就是说可以预测实例是否属于两个类中的一个。
逻辑回归用于广泛的应用，包括：
预测客户点击广告的概率。
预测患者患病的概率。
预测学生通过考试的概率。
预测贷款申请人违约贷款的概率。
逻辑回归是一种相对简单易懂的模型。它还具有计算效率高，
适合于大型数据集。
逻辑回归模型是一个sigmoid函数，即平滑的 S 形曲线。
sigmoid函数获取实值输入并输出 0 到 1 之间的概率。
逻辑回归模型使用最大似然估计过程拟合数据。该过程找到使
观察数据似然最大化的模型参数值。
拟合逻辑回归模型后，可以将其用于预测新数据的事件发生概
率。该模型还可以用来计算优势比，优势比是在两个不同组中事件
发生的优势之比。
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