dtGAP (version 0.0.2)

eval_tree: Evaluate Tree Model Predictions and Metrics

Description

Generates summary information and confusion matrix metrics for training and/or test subsets based on a fitted decision tree and sorted matrix results.

Usage

eval_tree(
  x = NULL,
  fit = NULL,
  task = c("classification", "regression"),
  tree_res = NULL,
  target_lab = NULL,
  sorted_dat = NULL,
  show = c("all", "train", "test"),
  model = c("rpart", "party", "C50", "caret", "cforest"),
  col_proximity = c("pearson", "spearman", "kendall"),
  linkage_method = c("CT", "SG", "CP"),
  seriate_method = "TSP",
  simple_metrics = FALSE
)

Value

A list with elements:

data_info

Character summary of dataset name, sizes, methods, and scores.

train_metrics

Character output of the train confusion matrix (if applicable).

test_metrics

Character output of the test confusion matrix (if applicable).

Arguments

x

Character. Name or label of the dataset.

fit

A fitted partykit tree object used to extract split variables.

task

Character. Type of task: "classification" or "regression".

tree_res

List. Output from compute_tree()

target_lab

Character. Name of the target column in tree_res$dat.

sorted_dat

List. Output from sorted_mat().

show

Character. "train","test", or "all" to select subset before sorting.

model

Character. Identifier for the model method (e.g., "rpart").

col_proximity

Character. Correlation method: "pearson","spearman","kendall".

linkage_method

Character. Linkage for supervised distance: "CT","SG","CP".

seriate_method

Character. Seriation method for distance objects; see seriation::list_seriation_methods("dist") for all supported options. Default: "TSP".

simple_metrics

Logical. If TRUE, use simple metric summary instead of full confusion matrix (default FALSE).

Examples

# \donttest{
library(rpart)
library(partykit)
library(ggparty)
library(dplyr)
library(seriation)
data_all <- add_data_type(
  data_train = train_covid, data_test = test_covid
)
data <- prepare_features(
  data_all,
  target_lab = "Outcome",
  task = "classification"
)
train_tree <- train_tree(
  data_train = train_covid,
  target_lab = "Outcome", model = "rpart"
)
fit <- train_tree$fit
var_imp <- train_tree$var_imp
tree_res <- compute_tree(
  fit,
  model = "rpart", show = "test",
  data = data, target_lab = "Outcome",
  task = "classification"
)
sorted_dat <- sorted_mat(
  tree_res,
  target_lab = "Outcome", show = "test"
)
# Case 1: Pass the dataset name
eval_tree(
  x = "covid", fit = fit,
  task = "classification",
  tree_res = tree_res,
  target_lab = "Outcome",
  sorted_dat = sorted_dat,
  show = "test", model = "rpart"
)
# }