Definition: Principal Component Analysis (PCA) is a dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving the most important information.  

Detailed Description:

PCA finds the principal components of a dataset, which are linear combinations of the original features. These principal components capture the most variance in the data. By projecting the data onto the principal components, you can reduce the dimensionality of the data without losing too much information.  

PCA is used in various applications, such as:

• Data visualization: Visualizing high-dimensional data in a lower-dimensional space.
• Feature engineering: Creating new features from existing features.
• Noise reduction: Removing noise from data.

Related Terms:

• Dimensionality Reduction
• Feature Engineering
• Data Visualization
• Eigenvectors
• Eigenvalues

FAQs:

1. How does PCA work?

   • PCA calculates the covariance matrix of the data and finds its eigenvectors and eigenvalues. The eigenvectors with the largest eigenvalues are the principal components.

2. What is the difference between PCA and t-SNE?

   • PCA is a linear dimensionality reduction technique, while t-SNE is a non-linear technique that preserves local structure in the data.

3. When should PCA be used?

   • PCA can be used when dealing with high-dimensional data, when there are redundant features, or when visualization is desired.