Logistic Regression 、 Linear Discriminant Analysis(LDA) 、 Quadratic Discriminant Analysis(QDA)

（1）
当分类边界是线性时， LR 和 LDA 会更好;
当分类边界是非线性时， QAD 会更好；
当分类边界更复杂时， KNN 会更好。

(2)
LR 和 LDA 都将产生线性分类边界，不同的是LR的系数估计是通过极大似然法， 而LDA系数是运用正态分布的均值和方差的估计值计算的， LR 适用于二分类问题，对于多分类问题，LDA 更为常见。

LDA 和 QDA 都是建立在自变量服从正态分布的假设上，所以当自变量的分布确实是几乎服从正态分布时，这两种方法表现的较好； LDA 和QDA 的区别在于LDA 假设所有类别的自变量都服从方差相同的正态分布，而QDA假设对于因变量属于不同类别的自变量服从方差不同的正态分布，选择LDA和QDA的关键在于bias-variance的权衡。

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