转载参考答案



I would say, the choice depends very much on what data you have and what is your purpose. A few "rules of thumb".

Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems.

Random Forest works well with a mixture of numerical and categorical features. When features are on the various scales, it is also fine. Roughly speaking, with Random Forest you can use data as they are. SVM maximizes the "margin" and thus relies on the concept of "distance" between different points. It is up to you to decide if "distance" is meaningful. As a consequence, one-hot encoding for categorical features is a must-do. Further, min-max or other scaling is highly recommended at preprocessing step.

If you have data with n points and m features, an intermediate step in SVM is constructing an n×n matrix (think about memory requirements for storage) by calculating n^2 dot products (computational complexity). Therefore, as a rule of thumb, SVM is hardly scalable beyond 10^5 points. Large number of features (homogeneous features with meaningful distance, pixel of image would be a perfect example) is generally not a problem.

For a classification problem Random Forest gives you probability of belonging to class. SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability.

For those problems, where SVM applies, it generally performs better than Random Forest.

SVM gives you "support vectors", that is points in each class closest to the boundary between classes. They may be of interest by themselves for interpretation.

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FROM 174.113.17.*

为啥要比这两个呢，感觉rf应该和gbm比
svm和这两个相去甚远，没啥比较的意义

【 在 MicroSat 的大作中提到: 】
: training集合训练
: testing集合预测
: training和testing是独立的。
: ...................
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FROM 125.33.244.*

性能我不太清楚
但rf和gbm都算是基于决策树的集成学习，二者可能更好比较一些

【 在 MicroSat 的大作中提到: 】
: 你是说svm的性能远不如rf，根本没有和rf比较的意义？
: rf应该和gbm比
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FROM 125.33.244.*

多谢！请问哪儿能找到这样的训练集和测试集，然后别人测试的精度值。
kagger不提供测试集的真实值。所以没法用来计算预测精度。

【 在 shong 的大作中提到: 】
: 我自己的经验，两者差不多，准确率也就是91%和92%这样的差别
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FROM 129.120.103.*