Foundations of Machine Learning Linear Model for ClassificationLinear Model for ClassificationLesson 4 - 1Linear Model for ClassificationLogistic Regression ( 逻辑回归或者对数几率回归）Tuning models with grid searchMulti-class classification ( 多分类学习 )Class-imbalance ( 类别不平衡 ) 问题Multi-label classification ( 多标签分类 )Linear Model for ClassificationLesson 5 - 2Logistic Regression ( 逻辑回归 )In Linear Regression, we discussed simple linear regression, multiple linear regression, and polynomial regression. These models are special cases of the generalized linear model ( 广义线性模型 ), a flexible framework that requires fewer assumptions than ordinary linear regression. In this lesson, we will discuss some of these assumptions as they relate to another special case of the generalized linear model called logistic regression.Linear Model for ClassificationLesson 5 - 3逻辑回归逻辑回归是一种将数据分类为离散结果的方法。分类问题其实和回归问题相似，不同的是分类问题需要预测的是一些离散值而不是连续值。 例如，我们可以使用逻辑回归将电子邮件分类为垃圾邮件或非垃圾邮件。能不能直接使用回归分析处理分类问题？Linear Model for ClassificationLesson 5 - 4Binary classification with logistic RegressionIn logistic regression, the response variable describes the probability that the outcome is the positive case. If the response variable is equal to or exceeds a discrimination threshold, the positive class is predicted; otherwise, the negative class is predicted. The response variable is modeled as a function of a linear combination of the explanatory variables using the logistic function.Linear Model for ClassificationLesson 5 - 5Cost Function我们不能在逻辑回归中使用和线性回归相同的 cost function ，因为其输出会是波动的，出现很多局部最小值，即它将不是‘凸函数’。Linear Model for Classif...
