Sage-Tips

Does principal component analysis work with 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. The only way PCA is a valid method of feature selection is if the most important variables are the ones that happen to have the most variation in them .

Can PCA be used for continuous variables?

PCA is designed for continuous variables. It tries to minimize variance (=squared deviations). The concept of squared deviations breaks down when you have binary variables. So yes, you can use PCA.

Can I use PCA for regression?

It affects the performance of regression and classification models. PCA (Principal Component Analysis) takes advantage of multicollinearity and combines the highly correlated variables into a set of uncorrelated variables. Therefore, PCA can effectively eliminate multicollinearity between features.

What type of data is used in PCA?

PCA is a very flexible tool and allows analysis of datasets that may contain, for example, multicollinearity, missing values, categorical data, and imprecise measurements.

Why is PCA not good?

PCA should be used mainly for variables which are strongly correlated. If the relationship is weak between variables, PCA does not work well to reduce data. In general, if most of the correlation coefficients are smaller than 0.3, PCA will not help.

What will happen if you apply PCA to a dataset that has a repeated feature?

Some information will be lost when the most important PC will be taken. And this information could be unbalanced across the subset of original features.

How to perform principal components analysis on categorical data?

You can convert categorical variables to a series of binary (0 or 1) variables and then perform principal components analysis on the result (assuming there is more than one categorical variable to start with).

Does PCA take into consideration categorical data?

While being very effective with numerical data, this algorithm cannot take into consideration categorical data as is. In this article, we will present FAMD, a generalization of PCA that takes into account both numerical and categorical variables, while giving each of these a similar importance regarding the production of the final components.

Does principal components analysis involve breaking down the variance structure?

A Machine Learning Engineer typically designs and builds AI algorithms to automate certain models, usually predictive models. An ML engineer also builds scalable solutions and too(Continue reading) No. Principal components analysis involves breaking down the variance structure of a group of variables.

What is factorial analysis of mixed data (FAMD)?

Let’s dive into the theory and implementation of FAMD, a very effective (yet misknown) technic adapting PCA to all types of variables. This article presents the Factorial Analysis of Mixed Data (FAMD), which generalizes the Principal Component Analysis (PCA) algorithm to datasets containing numerical and categorical variables.