Feature Selection Techniques in Machine Learning to Improve Your Model

When building a machine learning model in real-life, it's almost rare that all the variables in the dataset are useful to build a model. Adding redundant variables reduces the generalization capability of the model and may also reduce the overall accuracy of a classifier. Furthermore adding more and more variables to a model increases the overall complexity of the model. As per the Law of Parsimony of'Occam's Razor', the best explanation to a problem is that which involves the fewest possible assumptions. Thus, feature selection becomes an indispensable part of building machine learning models.