Feature Selection

TL;DR

Choose a smaller set of predictor variables to reduce model complexity and improve interpretability.
Proper selection can increase a model’s predictive accuracy by removing irrelevant or redundant predictors.
Common iterative methods are backward elimination (start with all features, remove insignificant ones) and forward selection (start with none, add significant ones).

Definition

Feature selection is the process of selecting a subset of relevant features (predictor variables) for use in model construction. It is an important step in the data preprocessing stage as it helps to reduce the complexity of the model, improve the interpretability of the results, and increase the model’s predictive accuracy.

Explanation

Feature selection aims to identify predictors that provide the most predictive power for a model and to remove irrelevant or redundant predictors. Two iterative selection approaches described in the source are:

Backward elimination: Begin with all candidate features included in the model. Evaluate each feature’s statistical significance (for example, using p-values). Remove features with p-values above a chosen threshold and repeat the process until all remaining predictors meet the significance criterion.
Forward selection: Begin with no predictors in the model. Add predictors one at a time, evaluating whether each candidate improves the model’s predictive accuracy. Keep predictors that improve accuracy and repeat until adding further predictors no longer improves the model.

The overall goal is to produce a simpler, more interpretable model that maintains or improves predictive performance by excluding irrelevant or redundant variables.

Examples

Backward elimination (house price)

Suppose we are building a model to predict the price of a house based on predictor variables such as number of bedrooms, square footage, and location. Using backward elimination, we would first build a model using all three predictors and then evaluate their statistical significance using a measure such as p-values. If the p-value for a predictor is above a certain threshold, we remove it from the model. This process is repeated until all remaining predictors have a p-value below the threshold.

Forward selection (customer churn)

Suppose we are building a model to predict the probability of a customer churning based on predictor variables such as customer age, number of products purchased, and average monthly spend. Using forward selection, we would first build a model using no predictors and then evaluate statistical significance by adding each predictor one at a time and measuring the improvement in the model’s predictive accuracy. If the predictor improves the model’s accuracy, we keep it and repeat the process with remaining predictors. This process is repeated until adding additional predictors does not improve the model’s accuracy.

Use cases

Data preprocessing to reduce model complexity
Improving interpretability of model results
Increasing a model’s predictive accuracy

Backward elimination
Forward selection