LinearTSVMSSLR.Rdmodel from RSSL package
Implementation of the Linear Support Vector Classifier. Can be solved in the Dual formulation, which is equivalent to SVM or the Primal formulation.
LinearTSVMSSLR( C = 1, Cstar = 0.1, s = 0, x_center = FALSE, scale = FALSE, eps = 1e-06, verbose = FALSE, init = NULL )
| C | Cost variable |
|---|---|
| Cstar | numeric; Cost parameter of the unlabeled objects |
| s | numeric; parameter controlling the loss function of the unlabeled objects |
| x_center | logical; Should the features be centered? |
| scale | Whether a z-transform should be applied (default: TRUE) |
| eps | Small value to ensure positive definiteness of the matrix in QP formulation |
| verbose | logical; Controls the verbosity of the output |
| init | numeric; Initial classifier parameters to start the convex concave procedure |
library(tidyverse) library(caret) library(tidymodels) library(SSLR) data(breast) set.seed(1) train.index <- createDataPartition(breast$Class, p = .7, list = FALSE) train <- breast[ train.index,] test <- breast[-train.index,] cls <- which(colnames(breast) == "Class") #% LABELED labeled.index <- createDataPartition(breast$Class, p = .2, list = FALSE) train[-labeled.index,cls] <- NA m <- LinearTSVMSSLR() %>% fit(Class ~ ., data = train) #Accuracy predict(m,test) %>% bind_cols(test) %>% metrics(truth = "Class", estimate = .pred_class)#> # A tibble: 2 x 3 #> .metric .estimator .estimate #> <chr> <chr> <dbl> #> 1 accuracy binary 0.694 #> 2 kap binary 0.0391