Dimensionality refers to how many input characteristics, variables, or columns are present in a given dataset, while dimensionality reduction refers to the process of reducing these features.
The dimensionality reduction technique on the given dataset has the benefits listed below:
- By reducing the dimensionality of the features, less storage space is required to store the dataset.
- Shorter calculation training times are required for features with lower dimension.
- The dataset's reduced-dimension features make it simpler to quickly visualise the data.
- The redundancy is eliminated because of the multicollinearity (if any are present).
The list of disadvantages of employing the dimensionality reduction additionally includes the following:
- Data loss could occur as a result of the reduction in dimensionality.
- In the PCA approach to reducing dimensionality, the key factors that must be taken into account are occasionally unknown.
