Sequential Backward Selection

Sequential backward selection (SBS) is a feature selection technique used in machine learning to pick the most relevant features from a dataset. It works by starting with all the features and gradually removing one feature at a time until the desired subset of features is obtained.

To understand SBS, let’s consider an example. Imagine you have a dataset with several features such as
age, income, education level, and occupation, and you want to predict whether a person is likely to
purchase a particular product.

In the beginning, SBS would start with all the features (age, income, education level, and occupation)
and build a model using these features. It evaluates the performance of the model, for example, by
measuring its accuracy or error rate.

Then, SBS would remove one feature, say “occupation,” and build another model using the remaining
features (age, income, and education level). It again evaluates the model’s performance and compares it to the previous model.

If removing “occupation” doesn’t significantly affect the model’s performance, SBS proceeds to remove another feature, such as “education level” It continues this process, iteratively removing one feature at a time, building models with the remaining features, and evaluating their performance.

The goal of SBS is to find the smallest subset of features that still provides good predictive performance. So, SBS continues removing features until either a desired level of performance is reached or until no more features can be removed without negatively impacting the model’s performance.

In our example, SBS might determine that age and income alone are sufficient to build an accurate model for predicting product purchase behavior. Therefore, it would select those two features as the final subset.

By using SBS, you can simplify your model and potentially improve its performance by focusing only on
the most relevant features, which can save computational resources and reduce the risk of overfitting.