Question - What is feature selection in big data?
Answer -
Feature selection refers to the process of extracting only specific information from a data set. This can reduce the amount of data that needs to be analyzed, while improving the quality of that data used for analysis. Feature selection makes it possible for data scientists to refine the input variables they use to model and analyze the data, leading to more accurate results, while reducing the computational overhead.
Data scientists use sophisticated algorithms for feature selection, which usually fall into one of the following three categories:
- Filter methods. A subset of input variables is selected during a preprocessing stage by ranking the data based on such factors as importance and relevance.
- Wrapper methods. This approach is a resource-intensive operation that uses machine learning and predictive analytics to try to determine which input variables to keep, usually providing better results than filter methods.
- Embedded methods. Embedded methods combine attributes of both the file and wrapper methods, using fewer computational resources than wrapper methods, while providing better results than filter methods. However, embedded methods are not always as effective as wrapper methods.