Principal Component Analysis (PCA)
Explore popular interview questions related to PCA approaches.
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Principal Component Analysis (PCA) is a powerful technique for reducing the dimensionality of datasets while preserving as much variance as possible. In this lesson, we'll explore the intuition behind PCA, understand its application in image compression, and use it to visualize high-dimensional data like the Wine dataset. Let’s get started.
What is PCA?
Your interviewer asks: What is Principal Component Analysis (PCA), and where is it useful?
This question is frequently asked at Meta, Apple, and DeepMind–especially for ML and CV-focused interviews. It is also relevant for ML Ops and tooling teams working on model deployment efficiency.
Sample answer
Principal Component Analysis (PCA) is a widely used linear dimensionality reduction technique that simplifies datasets with a large number of features, while preserving as much important information as possible. By identifying and ranking the principal components (orthogonal vectors) that explain the highest variance in the data, PCA allows for a compact representation of the dataset. This process reduces dataset's dimensionality, minimizes redundancy, and improves computational efficiency.
Dimensionality reduction through PCA is valuable for several reasons:
It simplifies high-dimensional datasets for easier analyses and visualization.
It reduces computational cost and storage requirements, particularly for data-intensive tasks.
It eliminates multicollinearity by removing correlated features, which can improve the performance of machine learning algorithms.
The process of PCA can be explained through the following structured steps: ...