Developing a predictive model, whether statistical or machine learning, requires mastering several aspects, from understanding business problems to feature engineering to the model’s final development, evaluation, and validation.
In this article, you will learn about one of the most important aspects of feature engineering- feature reduction. While there are numerous ways to reduce features, two of the most common and often confusing techniques that, on the surface, look very similar are Principal Component Analysis (PCA) and Factor Analysis.
This article will discuss factor analysis vs. PCA, their use cases, and how to apply these techniques. We shall also examine the difference between these two methods and decide which method to use: PCA or Factor Analysis.
Overview of Dimension Reduction Techniques
In our world, where decisions rely on structured data, there’s a danger of having too much information and risking the loss of important details.
For example, a model built to predict revenue using simple linear regression with advertising spend as the only variable will be unreliable. Adding more variables like marketing spend, procurement costs, and product categories improves the model and increases the data exponentially. This phenomenon, known as the ‘Curse of Dimensionality,’ makes the data sparse and difficult to manage, leading to potential overfitting and poor predictions for new data.
Moreover, including too many variables can cause multicollinearity, where some variables do not significantly contribute to the model, further increasing the risk of overfitting. Dropping these variables might lead to the loss of important information.
We use Dimensional Reduction techniques, such as Principal Component Analysis (PCA) and Factor Analysis, to address these issues. These methods reduce the number of features while preserving the essential information, helping to manage data sparsity and improve model accuracy without significant information loss.
Before diving deep into understanding PCA and Factor Analysis, a short note –
Course Alert 👨🏻💻
Learning and understanding technical concepts like PCA and Factor Analysis are easier with our tailor-made courses. With us, you will master this skill. Whether you are a new graduate or a working professional, we have data science courses with syllabi relevant to you.
Explore our signature data science courses in collaboration with Electronics & ICT Academy, IIT Guwahati, and join us for experiential learning to transform your career.
- Certification Course in Data Science
- Data Science with Python
- Data Science with R
- PG in Data Science
- Python Machine Learning
We have elaborate courses on AI, ML engineering, and business analytics. Choose a learning module that fits your needs—classroom, online, or blended eLearning.
Check out our upcoming batches or book a free demo with us. Also, check out our exclusive enrollment offers
What is Principal Component Analysis (PCA)?
Principal Component Analysis (PCA) is a feature reduction technique in an unsupervised learning setup. It removes dependency or redundancy in data by dropping features that contain the same information as given by other attributes. The derived components are independent of each other.
PCA reduces the unnecessary features in the data by creating or deriving new dimensions (also called components). These components are a linear combination of the original variables. This way, PCA converts more correlated variables (i.e., breaks down the data) into a smaller set of uncorrelated variables. A principal component of a data set is the direction with the largest variance.
Technically, PCA does this by rotating the axes of each variable. The axes are rotated to absorb all the information or the spread available in the variable. So, each ax is now a new dimension or principal component. The component is defined as the direction of the dataset explaining the highest variance, which is implied by the eigenvalue of that component. The rotation of the axis is graphically depicted.