Descriptions
Dimension reduction in R, Do You always work with datasets with an overwhelming number of features? Do you need all of these features? Which are the most important? In this course, you will learn dimensionality reduction techniques that will help you simplify your data and the models you build with your data, while retaining the information in the original data and good predictive performance. We live In The information Age-an era of information overload. The art of extracting essential information from data is a marketable skill. Models train faster on reduced data. In production, smaller models mean faster response time. Perhaps most importantly, smaller data and models are often easier to understand. Dimensionality reduction is your Occam’s razor in data science. The Difference between characteristics Selection and feature extraction! Using R, you will learn how to identify and remove features with little or redundant information while keeping the features with the most information. That’s feature selection. You will also learn how to extract combinations of features as compressed components that contain the maximum amount of information. That’s feature extraction!
What you will learn
- Foundations the dimension reduction
- Special feature Selection according to functional importance
- Special feature Selection for model performance
- Special feature Extraction and model performance
Specifications of dimensionality reduction in R
- Publisher : Data Camp
- Teacher: Matt Pickard
- Language: English
- Level: All levels
- Number of courses: 4
- Duration: 4 hours to complete the course
Content of dimension reduction in R
Requirements
Pictures
Sample clip
installation Guide
Extract the files and watch them with your favorite player
Subtitles: English
Quality: 720p
Download links
Password file(s): free download software
File size
90MB