Splits a dataframe into train and test dataframes for model evaluation. Prepared data include data objects for xgboost.
Details
This function performs k-fold cross-validation or a simple train-test split (if k=1) by splitting the input dataframe into k folds. Each fold serves as a test set once,while the remaining k-1 folds form the training set.
The function prepares data objects for xgboost model training and evaluation, including train, test, and validation datasets and xgboost DMatrix objects.
The output is a list containing the following elements: -`f`: List of train,test,and validation datasets for each fold. -`index`: Vector of fold indices. -`model_formula`: Model formula used for generating the datasets. -`variables`: Names of the variables in the model formula. -`predictors`: Names of the predictor variables. -`outcome`: Name of the outcome variable. -`xgb`: List of xgboost DMatrix objects for training,testing,and validation.
Examples
# Example with the 'infert' dataset
infert_formula<-formula(case~education+spontaneous+induced)
result<-k_sample(df=infert,k=10,model_formula=infert_formula)
#> Fold Cases: 1 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 2 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 3 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 4 Train: 12 Test: 6 Validation: 6 Total: 24 Unique Train: 12 Unique Test: 6 Unique Validation: 6
#> Fold Cases: 5 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 6 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 7 Train: 12 Test: 6 Validation: 6 Total: 24 Unique Train: 12 Unique Test: 6 Unique Validation: 6
#> Fold Cases: 8 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 9 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
#> Fold Cases: 10 Train: 12 Test: 6 Validation: 6 Total: 25 Unique Train: 12 Unique Test: 6 Unique Validation: 7
# Example with the 'mtcars' dataset
model_formula<-formula(mpg~cyl+disp+hp+drat+wt+qsec+vs+am+gear+carb)
result<-k_sample(df=mtcars,k=10,model_formula=model_formula)
#> Fold Cases: 1 Train: 2 Test: 1 Validation: 1 Total: 4 Unique Train: 2 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 2 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 3 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 4 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 5 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 6 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 7 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 8 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 9 Train: 1 Test: 1 Validation: 1 Total: 3 Unique Train: 1 Unique Test: 1 Unique Validation: 1
#> Fold Cases: 10 Train: 2 Test: 1 Validation: 1 Total: 4 Unique Train: 2 Unique Test: 1 Unique Validation: 1