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调整多元响应随机森林

[英]Tune multivariate response random forest

Is it possible to tune a random forest ( cforest ) with a multivariate response variable using caret ? 是否可以使用caret来调整具有多变量响应变量的随机森林( cforest )? eg 例如

mtry_grid <- data.frame(mtry = seq(5,50,5))
train_mtry_class <- train(Class+PRE_POST~., data=rf_data[,-c(1,2)],
                      method='cforest', tuneGrid=mtry_grid, metric='Accuracy')

If not, does anyone have any suggestions for tuning a random forest with a multivariate response? 如果不是,是否有人建议调整具有多变量响应的随机森林?

There's a great CRAN package, which you can do multivariate random forest tuning: https://cran.r-project.org/web/packages/MultivariateRandomForest/MultivariateRandomForest.pdf 有一个很棒的CRAN软件包,您可以执行多变量随机森林调整: https : //cran.r-project.org/web/packages/MultivariateRandomForest/MultivariateRandomForest.pdf

Alternatively, you can use "party" (also in CRAN): https://cran.r-project.org/web/packages/party/party.pdf - look at "Conditional Inference Trees" 另外,您也可以使用“ party”(也在CRAN中使用): https ://cran.r-project.org/web/packages/party/party.pdf-查看“条件推理树”

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