What are the key differences between PCA and Factor Analysis?
Understanding the distinctions between Principal Component Analysis (PCA) and Factor Analysis (FA) is crucial for data scientists who deal with dimensionality reduction techniques. Both methods aim to simplify complex data sets, but they approach this task from different angles and with varying assumptions. This article will explore the key differences between PCA and FA, providing you with a clearer picture of when to use each technique for optimal data analysis.
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Variance focus:Principal Component Analysis (PCA) spotlights total variance, extracting principal components that showcase the most variation within your data. This is ideal for pre-processing in machine learning.
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Latent variables:Factor Analysis (FA) is about digging deeper to find the why behind data correlations, revealing latent variables that aren't directly observed but influence your outcomes—great for hypothesis testing.