Feature selection is the process of selecting a subset of relevant features (variables, predictors) to use in a model. The goal of feature selection is to improve model performance by reducing overfitting, improving generalization, and enhancing computational efficiency.
Including all possible features can lead to overfitting, where the model captures noise instead of the underlying data patterns. Overfitting decreases a model's ability to generalize to new data. Additionally, more features increase memory usage and computation time during model training and prediction. Therefore, selecting a subset of meaningful features helps balance model complexity and performance.
Feature selection methods are primarily categorized into two types: model-agnostic and model-specific approaches. Model-agnostic methods can be applied to any model, while model-specific methods rely on the structure or properties of specific algorithms.
A straightforward model-agnostic approach is to exclude features with minimal variation. Features with little to no variation across observations contribute minimally to the prediction and can be considered uninformative. Removing such features reduces dimensionality without significantly impacting model performance.
Another popular model-agnostic method is regularization. By applying L1 regularization (Lasso) on a linear model, we add a penalty to the model's objective function that is proportional to the absolute values of the feature coefficients. This penalty encourages the model to assign zero coefficients to less relevant features, effectively removing them from the model.
Yet another approach to feature selection involves using a separate model to assess feature importance. This method allows us to leverage the strengths of certain algorithms to determine which features are most impactful. For example, models like random forest can rank features by importance. Once we identify the most influential features, we can use these features to build our own model with potentially enhanced performance.
Iterative feature selection methods evaluate model performance as features are added or removed, enabling the identification of the most impactful subset of features. These methods can operate in various ways:
See the scikit-learn documentation for further details on feature selection implementations.
Model-specific methods leverage the properties of specific algorithms to assess feature importance. For example, ensemble methods like random forest rank feature importance based on how often features are used to make decisions in the trees, with higher-ranked features contributing more to predictions.
Some models, especially in statistical learning, provide fitness scores like Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). These criteria measure model fit while penalizing complexity, making them useful for selecting models with the best balance of fit and simplicity. Models such as Hidden Markov Models (HMM) in hmmlearn often use AIC or BIC to optimize feature selection and ensure a good trade-off between accuracy and complexity.