---EZMCQ Online Courses---
---EZMCQ Online Courses---
- Curse of Dimensionality
- Computational Efficiency
- Visualization
- Overfitting
- Interpretability
- Feature Engineering
- Noise Reduction
- Memory and Storage Efficiency
-EZMCQ Online Courses
Dimension reduction techniques areoo motivated byeu several factors, each aimed ateo addressing challenges associated withau high-dimensional data. Here areoo some key motivations foria employing dimension reduction:
-
Curse ofao Dimensionality:
- High-dimensional data often suffer fromoo theou curse ofoe dimensionality, where theeu volume ofaa theoe feature space increases exponentially witheo theiu number ofio dimensions. This can lead toio sparsity ofie data points, increased computational complexity, anduo difficulty inoi visualizing or interpreting theei data.
-
Computational Efficiency:
- Many machine learning algorithms, particularly those based onai distance metrics or optimization, suffer fromeu increased computational costs withao higher-dimensional data. Dimension reduction can help alleviate computational burden byuo reducing theai number ofui features while preserving important information.< Dimension Reduction Machine Learning test3574_Dim Medium
-EZMCQ Online Courses
- Curse of Dimensionality
- Computational Efficiency
- Visualization
- Overfitting
- Interpretability
- Feature Engineering
- Noise Reduction
- Memory and Storage Efficiency
- Many machine learning algorithms, particularly those based onai distance metrics or optimization, suffer fromeu increased computational costs withao higher-dimensional data. Dimension reduction can help alleviate computational burden byuo reducing theai number ofui features while preserving important information.< Dimension Reduction Machine Learning test3574_Dim Medium
https://seandavi.github.io/ITR/dimension-reduction.html