Imagine that you are a nutritionist trying to explore the nutritional content of food. What is the best way to differentiate food items? By vitamin content? Protein levels? Or perhaps a combination of both?
Knowing the variables that best differentiate your items has several uses:
1. Visualization. Using the right variables to plot items will give more insights.
2. Uncovering Clusters. With good visualizations, hidden categories or clusters could be identified. Among food items for instance, we may identify broad categories like meat and vegetables, as well as sub-categories such as types of vegetables.
The question is, how do we derive the variables that best differentiate items?
Principal Components Analysis (PCA) is a technique that finds underlying variables (known as principal components) that best differentiate your data points. Principal components are dimensions along which your data points are most spread out:
A principal component…
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