0

我试图在目标是多类分类的数据集上在 R 中训练 ML 算法（rf、adaboost、xgboost）。对于超参数调整，我使用 MLR 包。

下面代码的目标是调整参数 mtry 和 nodesize，但将 ntrees 保持在 128（使用 mlrMBO）。但是，我收到以下错误消息。如何以正确的方式定义它？

rdesc <- makeResampleDesc("CV",stratify = T,iters=10L) 

traintask <- makeClassifTask(data = df_train, 
                             target = "more_than_X_perc_damage")
testtask <- makeClassifTask(data = df_test, 
                            target = "more_than_X_perc_damage")

lrn <- makeLearner("classif.randomForest",
                   predict.type = "prob")

# parameter space 
params_to_tune <- makeParamSet(makeIntegerParam("ntree", lower = 128, upper = 128),
                               makeNumericParam("mtry", lower = 0, upper = 1, trafo = function(x) ceiling(x*ncol(train_x))),
                               makeNumericParam("nodesize",lower = 0,upper = 1, trafo = function(x) ceiling(nrow(train_x)^x)))

ctrl = makeTuneControlMBO(mbo.control=mlrMBO::makeMBOControl())
tuned_params <- tuneParams(learner = lrn, 
                           task = traintask, 
                           control = ctrl, 
                           par.set = params_to_tune, 
                           resampling = rdesc, 
                           measure=acc)

rf_tuned_learner <- setHyperPars(learner = lrn,
                                 par.vals = tuned_params$x)

rf_tuned_model <- mlr::train(rf_tuned_learner, traintask)

# prediction performance
pred <- predict(rf_tuned_model, testtask)
performance(pred) 
calculateConfusionMatrix(pred)
stats <- confusionMatrix(pred$data$response,pred$data$truth)
acc_rf_tune <- stats$overall[1] # accuracy
print(acc_rf_tune)

(function (fn, nvars, max = FALSE, pop.size = 1000, max.generations = 100, : Domains[,1] 必须小于或等于 Domains[,2]

提前致谢！

4

1 回答 1

0

您可以通过不包括要保持不变的超参数来做到这一点，ParamSet而是在创建学习器时将其设置为您想要的值。

于 2021-07-22T15:30:18.037 回答