Data Science and Machine Learning (Theory and Projects) A to Z - Feature Selection: Search Strategy

The video tutorial explores feature subset generation methods, focusing on filter, embedded, and wrapper methods. It discusses the concept of solution space and the exponential growth of subsets with increasing features, highlighting the computational challenges. The tutorial explains NP-hard problems and introduces greedy solutions like forward selection and backward elimination, which are practical but not optimal. It also touches on alternative search strategies such as genetic algorithms and simulated annealing, emphasizing that no method guarantees a global optimum.

Discuss the role of criteria in both filter and wrapper methods.

Why is it important to consider the interaction between features in feature selection?

What is backward elimination and how does it differ from forward selection?

What are some alternative search strategies for feature selection beyond greedy methods?