View source: R/glasp_classification.R
glasp_classification | R Documentation |
parsnip interface for GLASP - Classification Models
glasp_classification( mode = "classification", l1 = NULL, l2 = NULL, frob = NULL, num_comp = NULL )
mode |
A single character string for the type of model. |
l1 |
Regularization in norm l1 (lasso) |
l2 |
Regularization in norm l2 (ridge) |
frob |
Regularization in Frobenius norm. It is unique to glasp and controls the importance of the clustering on the variables. If frob=0, we want to fit a model equivalent to elastic-net, without clustering. If frob > l1+l2 means that we are more interested on the feature clustering than the variable selection. If frob > 1 we are more interested on finding clusters than on getting a linear model with good predictive power. |
num_comp |
Maximum number of clusters to search. |
A glasp_classification
parsnip model
library(parsnip) library(yardstick) set.seed(0) data <- simulate_dummy_logistic_data() model <- glasp_classification(l1 = 0.05, l2 = 0.01, frob = 0.001, num_comp = 3) %>% set_engine("glasp") %>% fit(y~., data) pred = predict(model$fit, data, type="class") pred$.truth = data$y acc = accuracy(pred, truth=.truth, estimate=.pred_class) pred = predict(model$fit, data, type="prob") auc = roc_auc(pred, .pred_0, truth = data$y) #--- with tune ---- ## Not run: library(parsnip) library(tune) library(yardstick) library(rsample) set.seed(0) data <- simulate_dummy_logistic_data() model <- glasp_classification(l1 = tune(), l2 = tune(), frob = tune(), num_comp = tune()) %>% set_engine("glasp") data_rs <- vfold_cv(data, v = 4) hist <- tune_grid(model, y~., resamples = data_rs, metrics = metric_set(roc_auc, accuracy), grid = 100, control = control_grid(verbose = T, save_pred = T)) show_best(hist, 'roc_auc') show_best(hist, 'accuracy') ## End(Not run)
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