Introduce alookr

In alookr: Model Classifier for Binary Classification

knitr::opts_chunk$set(collapse = TRUE, comment = "")
options(tibble.print_min = 6L, tibble.print_max = 6L, width = 80)

Overview

Binary classification modeling with alookr.

Features:

• Clean and split data sets to train and test.
• Create several representative models.
• Evaluate the performance of the model to select the best model.
• Support the entire process of developing a binary classification model.

The name alookr comes from looking at the analytics process in the data analysis process.

Install alookr

The released version is available on CRAN. but not yet.

install.packages("alookr")

Or you can get the development version without vignettes from GitHub:

devtools::install_github("choonghyunryu/alookr")

Or you can get the development version with vignettes from GitHub:

install.packages(c("ISLR", "spelling", "mlbench"))
devtools::install_github("choonghyunryu/alookr", build_vignettes = TRUE)

Usage

alookr includes several vignette files, which we use throughout the documentation.

Provided vignettes is as follows.

• Cleansing the dataset
• Split the data into a train set and a test set
• Modeling and Evaluate, Predict

browseVignettes(package = "alookr")

How to use dlookr package

For information on how to use dlookr package, refer to the following website.

• Introduce alookr

alookr documentation built on June 12, 2022, 5:08 p.m.