What are dimensionality reduction and whats its benefits?

Data Science, AI and ML
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#1

Dimensionality reduction is the process of reducing the number of input variables in a dataset. As the number of features increases, your model becomes more complex, making a predictive modeling task more challenging.

Some of the advantages of dimensionality reduction include:

  • Higher model accuracy thanks to clean data.
  • Faster model training - fewer dimensions mean shorter computing time.
  • Reduced storage space needs.
  • Fewer dimensions allow usage of algorithms unfit for a large number of dimensions.
  • It helps remove redundant features.