如何在 R 中构建学习器或随机森林回归 [关闭]答案

【问题标题】：How to construct a learner or random forest regression in R [closed]如何在 R 中构建学习器或随机森林回归 [关闭]
【发布时间】：2021-11-25 07:03:50
【问题描述】：

使用 mlr 包的随机森林示例脚本用于分类问题。我有一个用于水污染的随机森林回归模型。我可以对连续目标变量进行分类，但我宁愿进行回归。如果有人知道使用 mlr 进行随机森林回归的脚本，请分享。我被困在学习者阶段：这些行都不起作用：

regr.lrn = makeLearner("regr.gbm", n.trees = 100)

regr.lrn = makeLearner("regr.randomForest", predict.type = "prob", fix.factors.prediction = TRUE)

【问题讨论】：

标签： r machine-learning random-forest mlr

【解决方案1】：

这个例子有帮助吗？

library(caret)
library(mlr)
library(MASS)

Boston1 <- Boston[,9:14]
datasplit <- createDataPartition(y = Boston1$medv, times = 1,
                                 p = 0.75, list = FALSE)

Boston1_train <- Boston1[datasplit,]
Boston1_test <- Boston1[-datasplit,]

rtask <- makeRegrTask(id = "bh", data = Boston1_train, target = "medv")
rmod <- train(makeLearner("regr.lm"), rtask)
rmod1 <- train(makeLearner("regr.fnn"), rtask)
rmod2 <- train(makeLearner("regr.randomForest"), rtask)

plotLearnerPrediction("regr.lm", features = "lstat", task = rtask)

plotLearnerPrediction("regr.fnn", features = "lstat", task = rtask)

plotLearnerPrediction("regr.randomForest", features = "lstat", task = rtask)

# what other learners could be used?
# lrns = listLearners()
rmod3 <- train(makeLearner("regr.cforest"), rtask)
rmod4 <- train(makeLearner("regr.ksvm"), rtask)
rmod5 <- train(makeLearner("regr.glmboost"), rtask)
plotLearnerPrediction("regr.cforest", features = "lstat", task = rtask)

plotLearnerPrediction("regr.ksvm", features = "lstat", task = rtask)

plotLearnerPrediction("regr.glmboost", features = "lstat", task = rtask)

pred_mod = predict(rmod, newdata = Boston1_test)
pred_mod1 = predict(rmod1, newdata = Boston1_test)
pred_mod2 = predict(rmod2, newdata = Boston1_test)
pred_mod3 = predict(rmod3, newdata = Boston1_test)
pred_mod4 = predict(rmod4, newdata = Boston1_test)
pred_mod5 = predict(rmod5, newdata = Boston1_test)

predictions <- data.frame(truth = pred_mod$data$truth,
                          regr.lm = pred_mod$data$response,
                          regr.fnn = pred_mod1$data$response,
                          regr.randomForest = pred_mod2$data$response,
                          regr.cforest = pred_mod3$data$response, 
                          regr.ksvm = pred_mod4$data$response,
                          regr.glmboost = pred_mod5$data$response)

library(tidyverse)
predictions %>%
  pivot_longer(-truth, values_to = "predicted_value") %>%
  ggplot(aes(x = truth, y = predicted_value, color = name)) +
  geom_smooth() +
  geom_abline(slope = 1, intercept = c(0, 0))

【讨论】：

请不要在答案中包含元评论（已编辑）；最好留下评论。但无论如何，一边回答问题一边争论主题问题不符合 SO 规则听起来很荒谬且前后矛盾。
好点 - 对不起 - 我试图提供帮助，但我可以看到我错过了标记。感谢您的编辑和评论