Feature Selection and Feature Engineering
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. Learning which features to select is a skill developed by data scientists that usually only comes from months and years of experience and can be more of an art than a science. Feature selection is important because it can:
- Shorten training times...
