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knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
For showing model fitting in SSLR
, we will use Wine dataset with 20% labeled data:
library(SSLR) library(tidymodels) library(caret)
knitr::opts_chunk$set( digits = 3, collapse = TRUE, comment = "#>" ) options(digits = 3) library(SSLR) library(tidymodels) library(caret)
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
In this package we have three functions to fit the different models:
We can use a formula with data (matrix or data.frame, with unlabeled data NAs in column to predict):
m <- SSLRDecisionTree() %>% fit(Wine ~ ., data = train)
We can use x data (matrix or data.frame) and y vector (factor or numeric, with unlabeled data NAs):
m <- SSLRDecisionTree() %>% fit_xy(x = train[,-cls], y = train$Wine)
We can use a x (matrix or data.frame) and y vector (factor or numeric, without NAs) and unalabeled data without y column (matrix or data.frame):
m <- SSLRDecisionTree() %>% fit_x_u(x = train[labeled.index,-cls], y = train[labeled.index,cls], x_U = train[-labeled.index,-cls])
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