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What does PCA stand for in the context of feature engineering?

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Question: What does PCA stand for in the context of feature engineering?

Options:

  1. Partial Component Analysis
  2. Principal Component Analysis
  3. Predictive Component Analysis
  4. Probabilistic Component Analysis

Correct Answer: Principal Component Analysis

Solution: 

PCA stands for Principal Component Analysis, a technique used for dimensionality reduction.

What does PCA stand for in the context of feature engineering?

Practice Questions

Q1
What does PCA stand for in the context of feature engineering?
  1. Partial Component Analysis
  2. Principal Component Analysis
  3. Predictive Component Analysis
  4. Probabilistic Component Analysis

Questions & Step-by-Step Solutions

What does PCA stand for in the context of feature engineering?
  • Step 1: Understand that PCA is an abbreviation.
  • Step 2: Know that PCA stands for Principal Component Analysis.
  • Step 3: Learn that PCA is a method used in data processing.
  • Step 4: Recognize that PCA helps reduce the number of features in a dataset.
  • Step 5: Understand that this reduction makes it easier to analyze and visualize data.
  • Principal Component Analysis – A statistical technique used to reduce the dimensionality of data while preserving as much variance as possible.
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