Practical Data Science: Reducing High Dimensional Data in R

4.36

Updated on

Course overview

Provider
Udemy
Course type
Paid course
Level
All Levels
Duration
3 hours
Lessons
11 lessons
Certificate
Available on completion
Course author
Manuel Amunategui
  • Understand various ways of reducing wide data sets
  • Understand Principal Component Analysis (PCA)
  • Control, tune and measure the effects of PCA
  • Use GBM modeling to measure the effectiveness of PCA
  • Reducing dimensionality with classic GBM & GLMNET Variable Selection
  • Use ensembling techniques to find the most stable variables

Description

In this R course, we'll see how PCA can reduce a 5000+ variable data set into 10 variables and barely lose accuracy!

In this R course, we'll see how PCA can reduce a 5000+ variable data set down to 10 variables and barely lose accuracy! We'll look at different ways of measuring PCA's effectiveness and other ways of reducing wide data sets (those with lots of features/variables). We'll also look at the advantages and disadvantages with different ways of reducing data.

Practical Data Science: Reducing High Dimensional Data in R
  • English language

  • Recommended provider

  • Certificate available