艳艳博客

Linear classifier1 Separable situationi SVM modelii Algorithm2 Un-separable situationi SVM modelii AlgorithmNonlinear classifier1 SVM model via kernel function2 SMO Algorithm1. Linear classif

Generative v DiscriminantGenerative learning algorithmTwo generative learning algorithm1 Gaussian discriminant analysis11 Core assumption of DGA12 GDA V logistic2 Naive Bayes21 Core assumption o

Neural NetworkCentral idea about neural networkUnderstanding of neural networkBack-Propagation algorithmNeural NetworkCentral idea about neural networkIn general, neural network is just a semi-param

Random ForestRandom Forest一Review of random forest二Models random forest can be used(一)Review of random forestFirstly, I give a review of two main ensemble algorithm: bagging and boosting:bagging bui

1.. 有四个数字：1、2、3、4，能组成多少个互不相同且无重复数字的三位数？各是多少？import itertoolsdef permutation12341(): result0= list(itertools.permutations('1234', 3)) result1 = [''.join(i) for i in result0] result2 = [int(i

1. XGBoost介绍XGBoost模型即是一些“串联”树结构的组合，最终预测结果由多棵树共同决定。 模型公式：y=f(z)=∑Kk=1fk(z)y=f(z)=∑k=1Kfk(z)y=f(z)=\sum_{k=1}^K f_k(z) 模型预测：y^i=∑Kk=1f^k(xi)y^i=∑k=1Kf^k(xi)\hat y_i = \sum_{k=1}^K \hat f_k(x_i...

Figure 10.2Firstly define the function to generate simulated data.# function for generating data for figure 10.2gen_eq_10_2_data <- function(N = 2000, p = 10){ X <- matrix( rnorm(N*p), ...