AI Machine Learning All-In-One Mastery Course by Brown Jamil

Author:Brown, Jamil
Language: eng
Format: epub
Published: 2024-07-08T00:00:00+00:00

2.2. Strengths and Limitations of PCA

Strengths:

● Simple and Efficient: Easy to implement and computationally efficient.

● Variance Preservation: Focuses on capturing the most variance in the data.

Limitations:

● Linearity Assumption: PCA assumes linear relationships between features.

● Interpretability: Principal components are often hard to interpret in real-world contexts.

3. Linear Discriminant Analysis (LDA)

3.1. Overview of LDA

Linear Discriminant Analysis (LDA) is another dimensionality reduction technique, often used for classification problems. Unlike PCA, which is unsupervised, LDA is supervised and focuses on finding the feature space that maximizes class separability.

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