What should the data science team do to meet these requirements?
Create new features and interaction variables.
Use a principal component analysis (PCA) model.
Apply normalization on the feature set.
Use a multiple correspondence analysis (MCA) model.
Explanations:
Creating new features and interaction variables might improve accuracy, but it is unlikely to reduce processing time or directly address the issue of high dimensionality.
Using Principal Component Analysis (PCA) reduces dimensionality by transforming features into principal components, which can help improve accuracy by removing noise and decrease processing time by reducing the number of variables.
Normalization can improve model performance in some cases, but it won’t directly address the issue of high dimensionality or reduce processing time.
Multiple Correspondence Analysis (MCA) is used for categorical data, not numeric data, so it wouldn’t be suitable for this scenario.