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For showing regression SSLR
models, we will use Airquality dataset with 10% labeled data:
library(SSLR) library(tidymodels)
knitr::opts_chunk$set( digits = 3, collapse = TRUE, comment = "#>" ) options(digits = 3) library(SSLR) library(tidymodels)
set.seed(1) data <- airquality #Delete column Solar.R (NAs values) data$Solar.R <- NULL #Train and test data train.index <- sample(nrow(data), round(0.7 * nrow(data))) train <- data[ train.index,] test <- data[-train.index,] cls <- which(colnames(airquality) == "Ozone") #% LABELED labeled.index <- sample(nrow(train), round(0.1 * nrow(train))) train[-labeled.index,cls] <- NA
For example, we can train with Decision Tree:
m <- SSLRDecisionTree(min_samples_split = round(length(labeled.index) * 0.25), w = 0.3) %>% fit(Ozone ~ ., data = train)
Now we can use metrics from yardstick
package:
predict(m,test)%>% bind_cols(test) %>% metrics(truth = "Ozone", estimate = .pred)
We can train with Random Forest:
m <- SSLRRandomForest(trees = 5, w = 0.3) %>% fit(Ozone ~ ., data = train)
For example, we can train with coBC:
m_r <- rand_forest( mode = "regression") %>% set_engine("ranger") m <- coBC(learner = m_r, max.iter = 1) %>% fit(Ozone ~ ., data = train)
We can train with COREG:
#Load kknn library(kknn) m_coreg <- COREG(max.iter = 1) %>% fit(Ozone ~ ., data = train)
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