What is the main goal of dimensionality reduction techniques like PCA?

₹0.0

Question: What is the main goal of dimensionality reduction techniques like PCA?

Options:

Correct Answer: To reduce the number of features while preserving variance

Solution:

PCA aims to reduce the number of features while preserving as much variance as possible in the data.

What is the main goal of dimensionality reduction techniques like PCA?

What is the main goal of dimensionality reduction techniques like PCA?

Step 1: Understand that data often has many features (or dimensions).
Step 2: Realize that having too many features can make analysis difficult and slow.
Step 3: Learn that dimensionality reduction techniques help simplify the data.
Step 4: Know that PCA (Principal Component Analysis) is one such technique.
Step 5: Understand that PCA reduces the number of features while keeping the important information.
Step 6: Remember that the goal is to keep as much variance (or information) in the data as possible.

No concepts available.