train_to_final_model: A function that combines cv_loop_train and cv_train_final

View source: R/classifier_train.R

train_to_final_modelR Documentation

A function that combines cv_loop_train and cv_train_final

Description

A function that combines cv_loop_train and cv_train_final

Usage

train_to_final_model(
  data,
  cls,
  stratify = NA,
  train_final = TRUE,
  train_consensus = TRUE,
  fitControl,
  K = 25,
  resampling_rate = 0.8,
  n_features = NA,
  filter_method = c("ROC", "WILCOX"),
  filter_direction = c("two.sided", "greater", "less"),
  filter_threshold_diff = 1,
  filter_threshold_score = 0.8,
  observation_weights = NULL,
  feature_weights = c("uniform", "weighted"),
  predictor_score_threshold = 0.1,
  verbose = FALSE
)

Arguments

data

input matrix, of dimension nobs x nvars; each row is an observation vector. Since this is an input to glmnet, it should be the format that can be used with glmnet

cls

class labels

fitControl

A list of training parameters. See caret::trainControl for detail

K

...

resampling_rate

...

n_features

...

filter_method

...

filter_direction

...

filter_threshold_diff

...

filter_threshold_score

...

observation_weights

...

feature_weights

...

predictor_score_threshold

...

Value

a list of cv_loop_trained (see cv_loop_train_iter), classification_results (see classification_summary_workflow), and final_cv_model (see cv_train_final and additional slots)


skimlab/CCSBUtils documentation built on March 30, 2022, 4:52 a.m.