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.

What is the primary focus of filter methods in feature selection?

• Generating all possible subsets

• Using individual feature rankings

• Combining features randomly

• Ignoring feature importance

How is the solution space defined in the context of feature selection?

• The complexity of the algorithm

• The time taken to evaluate features

• The number of possible subsets

• The total number of features

Why is the feature selection problem considered NP-hard?

• It requires polynomial time to solve

• It involves a small number of iterations

• It needs exponential iterations for the best solution

• It can be solved using simple algorithms

What is a key characteristic of greedy algorithms in feature selection?

• They are always computationally expensive

• They guarantee the optimal solution

• They provide a workable solution quickly

• They explore the entire solution space

In forward selection, how are features added to the subset?

• All at once

• One by one based on criteria

• By removing irrelevant features

• Randomly

What is a limitation of the forward selection method?

• It guarantees the best global solution

• It may discard useful features

• It requires no criteria for selection

• It is not used in practice

How does backward elimination differ from forward selection?

• It guarantees optimal solutions

• It adds features sequentially

• It starts with no features

• It removes features from the full set

What is a common trait of both forward selection and backward elimination?

• They are both optimal solutions

• They both explore the entire solution space

• They are both greedy strategies

• They are not used in practice

Which of the following is an alternative search strategy mentioned in the video?

• Linear regression

• Support vector machines

• Genetic algorithms

• Decision trees

Why are forward selection and backward elimination popular in practice?

• They require no criteria for selection

• They are complex and hard to implement

• They are simple and sequential

• They guarantee the best solution