Direct Preference Optimization (DPO)
Machine Learning
Techniques to reduce the number of features in data while preserving important information.
Detailed Explanation
Dimensionality Reduction is a set of techniques in machine learning aimed at decreasing the number of input features while retaining as much relevant information as possible. This process simplifies data, reduces computational costs, and can improve model performance by eliminating noise and redundancy. Common methods include Principal Component Analysis (PCA) and t-SNE, making data visualization and analysis more manageable.
Use Cases
- •Simplify high-dimensional data for visualization, improving model efficiency and performance by reducing redundancy and noise with techniques like PCA and t-SNE.
Related Terms
Other terms in the Machine Learning category