cv_model_with_auc: Perform Cross Validation for RFSLAM model

In dshenker/RFSLAM: What the Package Does (One Line, Title Case)

Perform Cross Validation for RFSLAM model

Description

cv_model_with_auc performs cross validation and returns the average auc value across the folds for the model

Usage

cv_model_with_auc(
  modeling_df,
  target,
  id_col,
  risk_time_col,
  patient_count_col = "int.n",
  time_col,
  n.folds,
  folds_stratifier,
  drop,
  ntree = 100,
  nodedepth = NULL,
  nsplit = 10
)

Arguments

modeling_df	dataframe containing data for modeling
target	name of the target variable column
id_col	name of the column with the patient id's
risk_time_col	name of the column with the risk time values
patient_count_col	name of the column with the patient counts
n.folds	the number of folds for cross validation
folds_stratifier	the variable to use for creating the folds
drop	the variables that need to be dropped before training the model
ntree	number of trees for random forest
nodedepth	the node depth for random forest
nsplit	the nsplit parameter for random forest

Value

the average weighted auc value across all of the folds

dshenker/RFSLAM documentation built on Sept. 18, 2022, 3:26 a.m.