compare_classifiers: Comparing Classifiers' Performance

In Curious-Joe/mixBag: Collection of miscellaneous useful utility functions

Description

Fits five commonly used classifiers on data and compare their performance based on AROC.

Usage

compare_classifiers(recipe, test_df, target_lab = Y)

Arguments

recipe	A parsnip recipe object.
test_df	Test data frame to test model performances.
target_lab	Label used in the target feature to indicate positive outcome. Default value is Y.

Details

• Make sure the recipe object entered is at pre-prepped stage

• Five models compared are: logistic regression, elastic net, random forest, support vector machine, xtreme gradient boosting.

Value

Returns following two things:

• A line plot showing comparative area under the ROC curve score

• A tibble containing the test data along with the predicted probability calculated by the five classifiers.

Examples

  library(tidymodels)
  split <- rsample::initial_split(wine, strata = quality_bin)
  train <- rsample::training(split)
  test <- rsample::testing(split)
  recipe <- recipes::recipe(quality_bin ~ ., data = train) %>%
  update_role(ID, new_role = 'identification') %>%
  step_string2factor(all_nominal()) %>%
  step_knnimpute(all_predictors()) %>%
  step_normalize(all_numeric())

  compare_classifiers(recipe = recipe, test_df = test, target_lab = 1)

Curious-Joe/mixBag documentation built on Aug. 30, 2021, 6:48 p.m.