What is feature selection?

Feature Selection is a method used to select the relevant features for the model to train on. We need feature selection to remove the irrelevant features which leads the model to under-perform.

Feature selection important for linear models. It can make model performance better through selecting the most importance features and remove irrelevant features in order to make a prediction and it can also avoid overfitting, underfitting and bias-variance tradeoff.

Here are some of the feature selections:

  • Principal Component Analysis
  • Neighborhood Component Analysis
  • ReliefF Algorithm

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