Dimensionality refers to how many input characteristics, variables, or columns are present in a particular dataset, while dimensionality reduction refers to the process of reducing these features.

In many circumstances, a dataset has a significant number of input features, which complicates the process of predictive modelling. For training datasets with a large number of features, it is extremely challenging to visualise or predict the future; hence, dimensionality reduction techniques must be used. %[]

The phrase "it is a manner of turning the higher quality dataset into lower dimensions dataset, guaranteeing that it gives identical information" can be used to describe the technique of "dimensionality reduction." These methods are frequently used in machine learning to solve classification and regression issues while producing a more accurate predictive model.

It is frequently used in disciplines like voice recognition, data processing, bioinformatics, etc. that handle high-dimensional data. Additionally, it can be applied to cluster analysis, noise reduction, and data visualisation.

Dimensional Reduction - Advantages and Disadvantages

Following are some advantages of using the dimensionality reduction technique on the provided dataset:

The space needed to keep the dataset is decreased by lowering the dimensionality of the features. Reduced feature dimensions call for shorter computation training times. The dataset's features with reduced dimensions make the data easier to visualise rapidly. By having to take care of the multicollinearity, it eliminates the redundancy (if any are present).

The following list of drawbacks of using the dimensionality reduction also includes:

The reduction in dimensionality may result in some data loss. Sometimes the primary components needed to consider in the PCA method for reducing dimensionality are unknown.