Introduction to Feature Attributes in Machine Learning-I

Introduction In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature Selection is a technique in which we want to decrease the number of features keeping the performance of our model same (or increasing it). It is a common perception in ML community that garbage in garbage out, so if we input noise in out model then our model is gonna return dubious outputs.