1.

What do you mean by feature selection?

Answer»

Feature selection is the process of identifying and selecting the most relevant FEATURES that can be input to the machine learning algorithms for the purpose of MODEL creation.

Feature selection techniques are used for the purpose of neglecting all the redundant or unrelated features as an input to the machine learning models by decreasing the number of input VARIABLES and narrowing down the features to only the desired relevant features. There are few advantages of using these feature selection techniques which are mentioned below.

  • Time is saved in order to train any machine learning model as after using feature selection techniques we get only subset of desired features
  • It leads to simpler models which are easy to explain compared to any complex models.
  • More the number of features, more is the volume of space REQUIRED which eventually limit the availability of data. As this technique helps to eliminate the unrelated features, it helps to reduce DIMENSIONALITY.


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