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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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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 representation 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.

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