Feature selection is the process of identifying the most important and informative features within a dataset. It is one of the most important steps of machine learning modeling pipeline, since it has significant impact on model performance and its predictive power.
Simple Visualization of Feature Selection
Benefits of performing feature selection:
- Improved model performance & reduced complexity (Curse of Dimensionality)
- Reduced training time
- Diminished risk of overfitting coming from uninformative & redundant features
- Simplified deployment processes and live data pipelines, an often underestimated advantage.
Despite its numerous benefits, feature selection can be overlooked during machine learning model development due to the tight deadlines in real-life projects and underestimation of its effects on performance. Also there are high number of feature selection methods and not knowing/deciding which one(s) to use may lead to the omission of this crucial step.
A potential solution to overcome time related challenges and to leverage the positive impact of feature selection on machine learning models is the implementation of reusable, partially or fully automated code.