说明：  Logistic regression （逻辑回归）是一种非线性回归模型，特征数据可以是连续的，也可以是分类变量和哑变量，是当前业界比较常用的机器学习方法，用于估计某种事物的可能性，主要的用途： 分类问题：如，反垃圾系统判别，通过计算被标注为垃圾邮件的概率和非垃圾邮件的概率判定； 排序问题：如，推荐系统中的排序，根据转换预估值进行排序； 预测问题：如，广告系统中CTR预估，根据CTR预估值预测广告收益；
(Logistic regression is a non-linear regression model. Characteristic data can be continuous or categorical and dummy variables. It is a commonly used machine learning method in the industry to estimate the possibility of something. The main purpose: Classification problem: for example, the anti-spam system is judged by calculating the probability of being marked as spam and the probability of non-spam; Ranking problem: For example, the ranking in the recommendation system is based on the conversion estimate; Forecasting issues: For example, the CTR estimate in the advertising system, predicting the advertising revenue based on the CTR estimate;)