"It is a strategy of turning the higher dimensions dataset into fewer dimensions dataset while guaranteeing that it gives similar information," says one definition. These methods are commonly used in machine learning to develop a more accurate predictive model when tackling classification problems.
For instance, we may combine Dum Dums and Blow Pops to examine all lollipops at once. In both of these cases, dimensionality reduction can aid. Dimensionality reduction can be accomplished in two ways: Selection of features: From the initial feature set, we select a subset of features.
It cuts down on the amount of time and storage space needed. The reduction of multicollinearity improves the interpretation of machine learning model parameters. When data is reduced to very low dimensions, such as 2D or 3D, it becomes easier to visualise. Reduce the number of variables in your space.
Depending on the method, dimensionality reduction might be linear or non-linear. The principal linear approach, often known as Principal Component Analysis, or PCA, is explored further down.
It's one of the most widely used programmes for exploratory data analysis and predictive modelling. The variance of each characteristic is taken into account by PCA since the high attribute indicates a good separation between the classes and so minimises dimensionality.