MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Dimensionality reduction refers to techniques for reducing the number of input variables in training data. When dealing with high dimensional data, it is often useful to reduce the dimensionality by projecting the data to a lower dimensional subspace which captures the “essence” of the data.

Detailed explanation-2: -Dimensionality reduction brings many advantages to your machine learning data, including: Fewer features mean less complexity. You will need less storage space because you have fewer data. Fewer features require less computation time.

Detailed explanation-3: -Answer-D) Dimensionality reduction reduces collinearity.

Detailed explanation-4: -Dimensionality reduction is very useful for factor analysis-This is a useful approach to find latent variables which are not directly measured in a single variable but rather inferred from other variables in the dataset. These latent variables are called factors.