Principal Component Analysis (PCA) is the dimensionality reduction technique which has so many utilities. PCA reduces the dimensions of a dataset by projecting the data onto a lower-dimensional subspace. For example, a 2D dataset could be reduced by projecting the points onto a line. Each instance in the dataset would then be represented by a single value, rather than a pair of values. In a similar way, a 3D dataset could be reduced to two dimensions by projecting variables onto a plane. PCA has the following utilities:
- Mitigate the course of dimensionality
- Compress the data while minimizing the information lost at the same time
- Principal components will be further utilized in the next stage of supervised learning, in random forest, boosting, and so on
- Understanding the structure of data with hundreds of dimensions can be difficult, hence, by reducing the dimensions to 2D or 3D, observations can be visualized easily
PCA can easily be explained with the following...