Nothing
## ----eval=FALSE---------------------------------------------------------------
#
# library(SSLR)
# library(tidymodels)
# library(caret)
## ----include=FALSE------------------------------------------------------------
knitr::opts_chunk$set(
digits = 3,
collapse = TRUE,
comment = "#>"
)
options(digits = 3)
library(SSLR)
library(tidymodels)
library(caret)
## ----wine, results="hide"-----------------------------------------------------
data(wine)
set.seed(1)
#Train and test data
train.index <- createDataPartition(wine$Wine, p = .7, list = FALSE)
train <- wine[ train.index,]
test <- wine[-train.index,]
cls <- which(colnames(wine) == "Wine")
# 20 % LABELED
labeled.index <- createDataPartition(wine$Wine, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA
## ----fit, results="hide"------------------------------------------------------
m <- SSLRDecisionTree(min_samples_split = round(length(labeled.index) * 0.25),
w = 0.3) %>% fit(Wine ~ ., data = train)
## ----testing------------------------------------------------------------------
test_results <-
test %>%
select(Wine) %>%
as_tibble() %>%
mutate(
dt_class = predict(m, test) %>%
pull(.pred_class)
)
test_results
## ----metrics------------------------------------------------------------------
test_results %>% accuracy(truth = Wine, dt_class)
test_results %>% conf_mat(truth = Wine, dt_class)
#Using multiple metrics
multi_metric <- metric_set(accuracy, kap, sens, spec, f_meas )
test_results %>% multi_metric(truth = Wine, estimate = dt_class)
## ----metrics_raw--------------------------------------------------------------
predict(m,test,"raw")
## ----metrics_prob-------------------------------------------------------------
predict(m,test,"prob")
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