随机森林

说明：  随机森林算法与 Bagging 算法类似，均是基于 Bootstrap 方法重采样，产生多个训练集。不同的是，随机森林算法在构建决策树的时候，采用了随机选取分裂属性集的方法 本程序中，将乳腺肿瘤病灶组织的细胞核显微图像的 10 个量化特征作为模型的输入，良性乳腺肿瘤和恶性乳腺肿瘤作为模型的输出。用训练集数据进行随机森林分类器的创建，然后对测试集数据进行仿真测试，最后对测试结果进行分析。(Similar to bagging algorithm, random forest algorithm is based on bootstrap resampling to generate multiple training sets. The difference is that the random forest algorithm uses the method of randomly selecting the split attribute set when constructing the decision tree In this program, 10 quantitative features of nuclear microscopic image of breast tumor tissue are taken as the input of the model, and the benign and malignant breast tumor are taken as the output of the model. The training set data is used to create the random forest classifier, then the test set data is simulated and the test results are analyzed.)

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随机森林\MexStandalone, 0 , 2019-03-11
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