---
title: "Classification Modeling"
author: "Choonghyun Ryu"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Classification Modeling}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r environment, echo = FALSE, message = FALSE, warning=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "")
```

## Preface
Once the data set is ready for model development, the model is fitted, predicted, and evaluated in the following ways:

* Cleansing the data set
* Split the data into a train set and a test set
* **Modeling and Evaluate, Predict**
    + **Modeling**
        + **Binary classification modeling**
    + **Evaluate the model**
        + **Predict test set using fitted model**
        + **Calculate the performance metric**
        + **Plot the ROC curve** 
        + **Tunning the cut-off**         
    + **Predict**
        + **Predict** 
        + **Predict with cut-off**  
        
The alookr package makes these steps fast and easy:

## How to perform modeling

Refer to the following website for information on performing modeling, evaluating, and predicting.

- [`Classification Modeling`](https://choonghyunryu.github.io/alookr_vignette/modeling.html)