Package index • splendid

All functions

boot_test(): Obtain OOB sample to use as test set

boot_train(): Recursively create training set indices ensuring class representation in every bootstrap resample

classification(): Multiclass classification

dummify(): Create dummy variables

error_632(): .632(+) Estimator for log loss error rate

evaluation(): Evaluation of prediction performance

hgsc: Gene expression data for High Grade Serous Carcinoma from TCGA

ova_classification(): One-Vs-All training approach

ova_prediction(): One-Vs-All prediction approach

prediction(): Class prediction on OOB set

sequential_train() sequential_pred(): Sequential Algorithm

splendid(): Ensemble framework for Supervised Learning classification problems

splendid_ensemble(): Combine classification models into an ensemble

discrimination_plot() reliability_plot() roc_plot(): Discriminating graphs

splendid_model(): Train, predict, and evaluate classification models

splendid_process(): Process data

split_data(): Split data into training and test sets

subsample(): Subsampling Imbalanced Data

var_imp(): Variable Importance