Making Predictions on Test Data after Principal Component Analysis in R
Analytics Vidhya — July 28, 2016
Introduction
This is an update of my previous article on Principal Component Analysis in R & Python. After having received several request on describing the process of model building with principal components, I’ve added an exclusive section of model building in R.
I came to know that R users often lost their way after doing PCA on train set. They become indecisive of next step i.e. how to use these components to make predictions on test data. I hope this article would help you understand PCA in detail and use it more frequently in your daily modeling process.
Read The Complete Article
Don’t forget to drop in your suggestions / opinions on this topic. Even if you find any part of PCA difficult to understand, you can ask me below.
You can test your skills and knowledge. Check out Live Competitions and compete with best Data Scientists from all over the world.
One thought on "Making Predictions on Test Data after Principal Component Analysis in R"
Afreen Begum says: August 02, 2016 at 3:41 pm
Hi, I would like to know that after doing PCA On train set how can i use these components for prediction and i want to find weightages of attributes after doing PCA. how can i proceed for giving weightages to the attributes after doing PCA????