viralx (version 1.3.0)

glob_knn_vis: Global Visualization of SHAP Values for K-Nearest Neighbor Model

Description

This function generates a visualization for the global feature importance of a K-Nearest Neighbors (KNN) model trained on HIV data with specified hyperparameters.

Usage

glob_knn_vis(vip_featured, hiv_data, knn_hyperparameters, vip_train, v_train)

Value

A visualization of global feature importance for the KNN model.

Arguments

vip_featured

The name of the response variable to explain.

hiv_data

The training dataset containing predictor variables and the response variable.

knn_hyperparameters

A list of hyperparameters for the KNN model, including:

• neighbors: The number of neighbors to consider.

• weight_func: The weight function to use.

• dist_power: The distance power parameter.

vip_train

The dataset used for training the KNN model.

v_train

The response variable used for training the KNN model.

Examples

library(dplyr)
set.seed(123)
hiv_data <- train2
knn_hyperparameters <- list(neighbors = 5, weight_func = "optimal", dist_power = 0.3304783)
vip_featured <- c("cd_2022")
vip_features <- c("cd_2019", "vl_2019", "cd_2021", "vl_2021", "vl_2022")
vip_train <- train2 |>
dplyr::select(rsample::all_of(vip_features))
v_train <- train2 |>
dplyr::select(rsample::all_of(vip_featured))
glob_knn_vis(vip_featured, hiv_data, knn_hyperparameters, vip_train, v_train)