Dimensionality Reduction and Feature Selection
Large datasets often contain redundant information that hinders model performance. It studies dimensionality reduction techniques such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Learners explore eigenvalues and eigenvectors to understand variance distribution. Feature selection approaches help simplify models and enhance interpretability. Visualization of high-dimensional data becomes more accessible. It supports efficient and effective modeling practices.
Reduction Strategies:
- Feature selection methods
- Projection-based compression
- Visualization of reduced spaces
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Dimensionality Reduction and Feature Selection Conference Speakers
Recommended Sessions
- Capstone Project
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