Dimensionality Reduction
The term “dimensionality” refers to a dataset’s number of features or input variables.
Techniques for reducing the number of input variables in a dataset are referred to as dimensionality reduction.
The curse of dimensionality, describes how adding more input features frequently makes it harder to model a predictive modelling problem.
Data visualisation frequently makes use of dimensionality reduction techniques and high-dimensional statistics. Nevertheless, similar methods can be applied to classification or regression datasets in applied machine learning to make them easier to incorporate into a prediction model.
With too many input variables, machine learning algorithms’ performance may suffer.
The columns that are provided as input to a model to forecast the target variable are the input variables if your data is organised using rows and columns, as it would be in a spreadsheet. Features are another name for input variables.
The rows of data can be thought of as points in an n-dimensional feature space, and the columns of data as the dimensions on that space. This geometric interpretation of a dataset is helpful.
Having a large number of dimensions in the feature space can mean that the volume of that space is very large, and in turn, the points that we have in that space (rows of data) often represent a small and non-representative sample.
Benefits of applying Dimensionality Reduction
Some benefits of applying dimensionality reduction technique to the given dataset are given below:
- By reducing the dimensions of the features, the space required to store the dataset also gets reduced.
- Less Computation training time is required for reduced dimensions of features.
- Reduced dimensions of features of the dataset help in visualizing the data quickly.
- It removes the redundant features (if present) by taking care of multicollinearity.
