Principal Component Analysis (PCA)
Machine Learning · Further Education · Unsupervised Learning
Principal Component Analysis (PCA) is a dimensionality reduction technique that identifies the directions of maximum variance in data. By understanding PCA, you'll gain insights into data distribution and effectively reduce its dimensionality while preserving key information.
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Das gehört dazu
- Concept of PCA and its role in dimensionality reduction
- Mathematical foundations of PCA: variance
- eigenvectors
- and eigenvalues
- Steps involved in performing PCA on a dataset
- Determining the number of principal components to retain
- Applications of PCA in various domains
- Advantages and limitations of using PCA
Stichworte
- PCA
- Dimensionality Reduction
- Variance
- Eigenvectors
- Eigenvalues
- Data Distribution
- Unsupervised Learning
- Machine Learning