Implementing differnt aspects of Machine learning in this Repository. Contributions are welcome
Feature selection – also known as variable selection, attribute selection, or variable subset selection – is a method used to select a subset of features (variables, dimensions) from an initial dataset.
Feature selection is a key step in the process of building machine learning models and can have a huge impact on the performance of a model. Using correct and relevant features as the input to your model can also reduce the chance of overfitting, because having more relevant features reduces the opportunity of a model to use noisy features that don't add signal as input.
Lastly, having less input features decreases the amount of time that it will take to train a model.