Handling Class Imbalance Problem in R: Improving Predictive Model Performance | Unbalanced Dataset

Provides steps for carrying handling class imbalance problem or datasets that are unbalanced when developing classification and prediction models
R file: https://github.com/bkrai/R-files-from...
data: binary.csv available from github link above
Timestamps:
00:00 Introduction
00:05 Admit Data
01:37 What is class Imbalance Problem ?
03:26 Data Partition
04:17 Data for Predictive Model
05:28 Prediction Model - Random Forest
06:16 Model Evaluation with Test Data, confusion matrix
12:24 Oversampling for Better Sensitivity
16:13 Undersampling
18:19 Both Oversampling and Undersampling
20:08 Synthetic sampling using random over sampling examples

predictive models are important machine learning and statistical tools related to analyzing big data or working in data science field.

R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.