机器学习随机森林源码。改变决策树的深度对比分类结果。对鸢尾花数据进行决策树分析-random forest

Fisher判别适合于两类的判别分析。本文采用的鸢尾花数据库中鸢尾花类别有三类，所以先采用Fisher判别对数据进行二分类判别分析，然后采用一对一进行多分类。(Fisher discriminant analysis is suitable for two kinds of discriminant analysis. There are three categories of iris in the iris database in this paper, so the Fisher disc

利用fisher判别分析对于鸢尾花数据集进行分类(Fisher discriminant analysis was used to classify iris data sets)

用决策树-回归分析模型来分析鸢尾花数据，训练后最终可以得到模型的准确率(Using decision tree-regression analysis model to analyze iris data, the accuracy of the model can be obtained after training.)

MATLAB 利用Fisher分析和核Fisher分析对鸢尾花数据集进行分类，可以发现Kfisher 可以较好地对非线性数据的分类(MATLAB USES Fisher analysis and core Fisher analysis to classify the iris data set, and it can be found that Kfisher can classify the nonlinear data well)