The process of reducing the dimensions of a dataset while preserving as much variance as possible is known as ________.
- Principal Component Analysis
- Random Sampling
- Mean Shift
- Agglomerative Clustering
Dimensionality reduction techniques like Principal Component Analysis (PCA) are used to reduce the dataset's dimensions while preserving variance. PCA identifies new axes (principal components) in the data to reduce dimensionality. Hence, "Principal Component Analysis" is the correct answer.
Loading...
Related Quiz
- What is the primary benefit of using transfer learning in deep learning models?
- What is the central idea behind using autoencoders for anomaly detection in data?
- In the context of machine learning, what is the main difference between supervised and unsupervised learning in terms of data?
- A real estate company wants to predict the selling price of houses based on features like square footage, number of bedrooms, and location. Which regression technique would be most appropriate?
- If you're working with high-dimensional data and you want to reduce its dimensionality for visualization without necessarily preserving the global structure, which method would be apt?