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6.8000000e+001 8.9000000e+001 9.0000000e+001 6.8000000e+001 6.8000000e+001 8.5000000e+001 9.0000000e+001 7.2000000e+001 6.8000000e+001 8.5000000e+001 8.6000000e+001 6.8000000e+001 6.8000000e+001 7.9000000e+001 8.7000000e+001 6.3000000e+001 6.8000000e+001 7.9000000e+001 8.7000000e+001 6.7000000e+001 7.1000000e+001 8.3000000e+001 8.7000000e+001 6.7000000e+001 7.1000000e+001 7.5000000e+001 8.5000000e+001 6.2000000e+001 6.7000000e+001 7.9000000e+001 8.1000000e+001 6.2000000e+001 7.1000000e+001 7.9000000e+001 8.5000000e+001 6.2000000e+001 4.0000000e+000
6.8000000e+001 8.5000000e+001 9.0000000e+001 7.2000000e+001 6.8000000e+001 8.5000000e+001 8.6000000e+001 6.8000000e+001 6.8000000e+001 8.9000000e+001 8.6000000e+001 7.2000000e+001 6.8000000e+001 7.9000000e+001 8.7000000e+001 6.7000000e+001 7.1000000e+001 8.300
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随机森林是一种一种分类算法,属于集成学习中的Bagging算法,即引导聚合类算法,由于不专注于解决困难样本,所以模型的performance会受到限制。在学习随机森林算法之前,首先要弄懂三个概念:决策树;集成学习(Ensemble Learning)[多分类系统];自主采样法(Boostrap Sampling)。 随机森林是一个包含多个决策树的分类器,并且其输出的类别是由个别树输出的类别的众数而定。随机森林属于机器学习的一大分支——集成学习(EnsembleLearning)方法。随机森林具有对于很多种资料,可以产生高准确度的分类器;可以处理大量的输入变数;可以在决定类别时,评估变数的重要性;可以在内部对于一般化后的误差产生不偏差的估计;对于不平衡的分类资料集来说,可以平衡误差等优点。 注意:这份源码有调用到一个封装的32位的库,必须是32位的matlab才能运行成功
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5、Random Forest.zip (10个子文件)
5、Random Forest
main.m 3KB
runRF.m 3KB
train_data.txt 55KB
srbct.txt 978KB
runRF_demo.m 547B
RF.mexw32 80KB
satimage.tra 514KB
trainData.txt 1.13MB
satimage.txt 231KB
datas
satimage
test_data.txt 27KB
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