Database Reference
In-Depth Information
Using a dimensionality reduction model
It is interesting to be able to visualize the outcome of a model in this way; however, the
overall purpose of using dimensionality reduction is to create a more compact representa-
tion of the data that still captures the important features and variability in the raw dataset.
To do this, we need to use a trained model to transform our raw data by projecting it into
the new, lower-dimensional space represented by the principal components.
