Discover the 3 Key Reasons for Feature Selection in Machine Learning

Feature selection in machine learning models for tabular data is crucial for three main reasons: enhancing model accuracy, reducing computational costs, and improving model robustness. Firstly, using fewer features often leads to higher model accuracy. For instance, tree-based models can be confused by irrelevant features, especially as the tree depth increases, leading to less accurate predictions. Secondly, feature selection significantly cuts down the time and resources needed for model tuning, training, evaluation, and inference. For example, reducing the number of features can make the tuning process, which involves training numerous models, much faster. Lastly, while not detailed in this article, feature selection contributes to model robustness, ensuring performance stability over time. The article also introduces History-based Feature Selection (HBFS), a method that learns from past feature subset performances to estimate and discover better feature combinations, offering a balance between exploration and exploitation in feature selection.

Source: towardsdatascience.com