Contents
Which method is suitable for data reduction?
One of the methods used for data reduction is sampling as it is capable to reduce the large data set into a much smaller data sample.
How do you reduce the size of a categorical variable?
Dimensionality Reduction Techniques
- Principal component analysis (PCA)
- Correspondence analysis (CA)
- Multiple correspondence analysis (MCA)
- Multiple factor analysis (MFA)
- Factor analysis of mixed data (FAMD)
Can I apply PCA to categorical variables?
While it is technically possible to use PCA on discrete variables, or categorical variables that have been one hot encoded variables, you should not. Simply put, if your variables don’t belong on a coordinate plane, then do not apply PCA to them.
Why is variable reduction important in data science?
Variable reduction is a crucial step for accelerating model building without losing the potential predictive power of the data.
How to do dimensionality reduction in categorical data?
Amelia includes some limited capacity to deal with ordinal and nominal variables. As for dimensionality reduction for categorical data (i.e. a way to arrange variables into homogeneous clusters), I would suggest the method of Multiple Correspondence Analysis which will give you the latent variables that maximize the homogeneity of the clusters.
What can you do with a nominal level of data?
At a nominal level, each response or observation fits only into one category. Nominal data can be expressed in words or in numbers. But even if there are numerical labels for your data, you can’t order the labels in a meaningful way or perform arithmetic operations with them.
Can you code a nominal variable with a number?
You can code nominal variables with numbers, but the order is arbitrary and arithmetic operations cannot be performed on the numbers. This is the case when a person’s phone number, National Identification Number postal code, etc. are being collected.