rmachine-learningrandom-forestmlr

How to construct a learner or random forest regression in R

Example scripts for random forest using the mlr package are for classification problems. I have a random forest regression model for water contamination. I could classify the continuous target variable but I would rather run a regression. If anyone knows a script for random forest regression using mlr please share. I am stuck at the make a learner stage: neither of these lines work:

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

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

  • Does this example help?

    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)

    example_1.png

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

    example_2.png

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

    example_3.png

    # 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)

    example_4.png

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

    example_5.png

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

    example_6.png

    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))

    example_7.png