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Regression modeling

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)
#> # A tibble: 3 x 3
#>   .metric .estimator .estimate
#>   <chr>   <chr>          <dbl>
#> 1 rmse    standard      29.8  
#> 2 rsq     standard       0.525
#> 3 mae     standard      18.6

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)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.